Methods and Apparatus for Enhanced Product Recommendations

ABSTRACT

Apparatus and methods are given for providing enhanced product recommendations. In one embodiment, products are recommended to a consumer based on information manually entered by the consumer and/or derived from data relating to the consumer obtained from a health-monitoring platform. The recommended products are provided from a recommendation engine to a human operator or curator at an item selection entity, the curator selects a predetermined number of the recommended products for delivery to the consumer. Such delivery is established by the consumer to occur periodically (such as monthly) as part of a subscription service. Product recommendations are adjusted over time to relate to customer activity and interest data accumulated from the health-monitoring platform. In one embodiment, a recommendation engine is configured to specifically tailor its recommendation algorithms based on feedback information received from the curator and/or the consumer.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

The present disclosure relates to the field of product recommendations.More particularly, the present disclosure relates to methods, devices,systems, and computer programs for providing enhanced productrecommendations using a feedback-based learning mechanism.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Unless otherwiseindicated herein, the materials described in this or any section of thedisclosure are not prior art to the claims in this application and arenot admitted to be prior art by inclusion herein.

Online shopping provides a mechanism to enable consumers to peruseproducts for purchase at their convenience. Via online shoppingmechanisms, consumers can read detailed product descriptions, examinepictures of the items, and read other consumer's reviews in order tomake a fully informed purchase decision. Accordingly, every major sellerof goods has items available for purchase online through brand publishedwebsites and/or secondary retailer websites. Additionally, certainsellers provide online exclusive products, i.e., products that are onlyavailable for purchase online, and/or an online-only outlet fordiscounted or discontinued products. As the popularity of onlineshopping increases, consumers are often overwhelmed by the vast quantityof available products and navigating this product landscape can oftenfrustrate or confuse a consumer.

Moreover, it is not uncommon for a consumer, when faced with thisseemingly unending supply of products, to forget or misremember itemshe/she intended to purchase. That is, when navigating an online store,the consumer may become distracted (such as by environmental or externalfactors and/or by content on the website) and therefore less likely toremember to purchase items which were intended to be purchased and/orrepurchase items which may need replacement.

To assist in online shopping, certain prior art recommendation engineshave been utilized to provide a consumer with a manageable list ofproducts once the consumer has entered one or more criteria. However,such recommendation engines are often unable to generate recommendationswhich are tailored to a particular consumer in any meaningful way (otherthan based on the consumer's manually entered criteria). For example,the user must enter one or more criteria for filtering the vast numberof available products, such as by price, size, color, etc.

In addition, a customer may subscribe to a monthly subscription serviceto receive product recommendations. However, product recommendations inmonthly subscription services fail to uniquely target a particularcustomer. Instead such monthly services often merely provide a set orgroup of products to a group of only superficially similar customers.For example, beauty related subscription services may group usersaccording to gender, skin tone/color, hair color, eye color, and generalstyle; each of the foregoing being manually entered by the consumer whenestablishing a subscription.

Therefore, what is needed is a system for recommending products forpurchase based on information regarding the consumer obtained in othersocial media platforms which directly relate to the recommended product.Ideally, the consumer would perform minimal manual entry of profiledetails (if any) in order to receive highly targeted products forpurchase. Moreover, the ideal recommendation system would be configuredto learn or improve recommendations over time via and advanced feedbacksystem. Apparatus and methods for accomplishing the foregoing areprovided in the present disclosure.

SUMMARY OF THE DISCLOSURE

The present disclosure addresses the foregoing needs by disclosing,inter alia, methods, devices, systems, and computer programs for productrecommendations.

Specifically, methods, apparatus, computer applications, and systems areprovided to provide enhanced product recommendations using afeedback-based learning mechanism.

These and other aspects of the disclosure shall become apparent whenconsidered in light of the disclosure provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be readily understood by the following detaileddescription in conjunction with the accompanying drawings. To facilitatethis description, like reference numerals designate like structuralelements. Embodiments are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1A is a block diagram illustrating an exemplary network forproviding enhanced product recommendations in accordance with oneembodiment of the present disclosure.

FIG. 1B is a block diagram illustrating another embodiment of anexemplary network for providing enhanced product recommendations inaccordance with the present disclosure.

FIGS. 2A-2B are graphical illustrations of various pages of an exemplarycurator interface for use in providing enhanced product recommendationsin accordance with one embodiment of the present disclosure.

FIG. 3A is a logical flow diagram illustrating a generalized method forenabling a subscriber to create a subscriber account in accordance withone embodiment of the present disclosure.

FIG. 3B is a logical flow diagram illustrating a generalized method forestablishing a subscription for receiving curator-selected andcomputer-recommended products in accordance with one embodiment of thepresent disclosure.

FIG. 3C is a logical flow diagram illustrating a generalized method forproviding enhanced computer-recommendation of products in accordancewith one embodiment of the present disclosure.

FIG. 3D is a logical flow diagram illustrating a generalized method forfulfilling a curator-selected order of computer-recommended products inaccordance with one embodiment of the present disclosure.

FIG. 3E is a logical flow diagram illustrating a generalized method ofenabling feedback regarding curator selected ones ofcomputer-recommended products in accordance with one embodiment of thepresent disclosure.

FIG. 3F is a logical flow diagram illustrating a generalized method forproviding enhanced product recommendations in accordance with oneembodiment of the present disclosure.

FIG. 4 is a block diagram illustrating a generalized workflow forproviding enhanced product recommendations in accordance with anotherembodiment of the present disclosure.

FIG. 5 is a logical flow diagram illustrating an exemplary embodiment ofa method for providing enhanced product recommendations in accordancewith another embodiment of the present disclosure.

FIG. 6 is a block diagram illustrating an exemplary recommendationengine configuration for providing enhanced product recommendations inaccordance with one embodiment of the present disclosure.

FIG. 7 is a block diagram illustrating an exemplary curator apparatusconfiguration for providing enhanced product recommendations inaccordance with one embodiment of the present disclosure.

All Figures © Under Armour, Inc. 2016. All rights reserved.

DETAILED DESCRIPTION Exemplary Embodiments

Disclosed embodiments include systems, apparatus, methods and storagemedium associated with product recommendation in general, and inparticular enabling enhanced product recommendations using afeedback-based learning mechanism.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof wherein like numeralsdesignate like parts throughout, and in which is shown, by way ofillustration, embodiments that may be practiced. It is to be understoodthat other embodiments may be utilized, and structural or logicalchanges may be made without departing from the scope of the presentdisclosure. Therefore, the following detailed description is not to betaken in a limiting sense, and the scope of embodiments is defined bythe appended claims and their equivalents.

Aspects of the disclosure are disclosed in the accompanying description.Alternate embodiments of the present disclosure and their equivalentsmay be devised without parting from the spirit or scope of the presentdisclosure. It should be noted that any discussion herein regarding “oneembodiment”, “an embodiment”, “an exemplary embodiment”, and the likeindicate that the embodiment described may include a particular feature,structure, or characteristic, and that such particular feature,structure, or characteristic may not necessarily be included in everyembodiment. In addition, references to the foregoing do not necessarilycomprise a reference to the same embodiment. Finally, irrespective ofwhether it is explicitly described, one of ordinary skill in the artwould readily appreciate that each of the particular features,structures, or characteristics of the given embodiments may be utilizedin connection or combination with those of any other embodimentdiscussed herein.

Various operations may be described as multiple discrete actions oroperations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description should notbe construed as to imply that these operations are necessarily orderdependent. In particular, these operations may not be performed in theorder of presentation. Operations described may be performed in adifferent order than the described embodiment. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B and C).

The terms “comprising,” “including,” “having,” and the like, as usedwith respect to embodiments of the present disclosure, are synonymous.

Exemplary Networks

Referring now to FIG. 1A, an exemplary network 100 for enabling advancedproduct recommendations in accordance with one embodiment of the presentdisclosure is illustrated. Communication between each of the devices inthe network 100 illustrated in FIG. 1A is enabled via the communicationnetwork 108. Specifically, a recommendation engine 104 is placed incommunication with a curator apparatus 106 and one or more user dataservers 102 via a network 108. In addition, the curator apparatus 106 isplaced in communication with a plurality of user devices 110 and aproduct warehouse 112 via the network 108. The product warehouse 112 mayfurther communicate to the recommendation engine 104 in one embodiment.Each of the foregoing components will be discussed in further detailbelow.

The communication network 108 which enables communication between theaforementioned devices/entities may comprise a wired and/or wireless,private and/or public network, including e.g., the Internet.Accordingly, each of the devices (e.g., user data servers 102,recommendation engine 104, curator apparatus 106, user devices 110 andproduct warehouse 112) is configured with appropriate networkingcommunication interfaces. An example of wired communication interfacemay include, but is not limited to, Ethernet; while examples of wirelesscommunication interfaces may include, but are not limited to, near fieldcommunication (NFC), Bluetooth, WiFi, 4G or 5G LTE. It is furtherappreciated that various gateways, routers, switches, based stations,and so forth may be placed between the communication interfaces offoregoing devices.

The user device 110 (or client/consumer device) comprises a stationaryor portable computing apparatus which is configured to run a pluralityof software applications thereon (as discussed in detail below). Forexample, the user device 110 may comprise a desktop computer (such asthose available from Dell Computing of Austin, Tex.), or smartphone,computing tablet, laptop computer, electronic reader, personal digitalassistant, and so forth. Exemplary embodiments include e.g., Galaxy S4®from Samsung Electronics of Seoul, Korea, iPhone® or iPad® from AppleComputer of Cupertino, Calif. As illustrated in FIG. 1A, the user orconsumer device 110 is associated to a user or consumer location 114 forshipping purposes (discussed below); however, as noted the device 110may be mobile and/or not necessarily located at the user location 114.

In one embodiment, the user device 110 is in communication with aplurality of health monitoring devices (not shown) and/or the userdevice 110 may further comprise a health monitoring device. Heathmonitoring devices comprise portable computing devices designed tomeasure, sense, monitor, or otherwise receive biometric, environmental,and/or activity parameters. In one variant, the health monitoringdevices may comprise wearable health-related parameter measurement andcomputing devices, such as e.g., a smart watch, an activity tracker, aheart rate monitor, a sleep tracking device, a nutrition trackingdevice, a smart scale, and/or smart eyeglasses. In addition, user device110 may comprise a smart phone having one or more of the foregoingcapabilities and/or which enables user entry of the foregoing healthparameters.

The user device 110 receives health-parameter related data collected orobtained from the monitoring devices (not shown) via the network 108 inone embodiment. Alternatively, or in addition, health-parameter relateddata may be collected or obtained at the device 110 itself. The sensedor obtained data comprises data which the particular device isconfigured to collect (such as activity, biometric, and environmentaldata). For example, an activity tracking device is configured to collectactivity data such as steps taken, distance traveled, rate or pace of arun, and/or flights of stairs climbed, etc.; a heart rate monitor isconfigured to collect heartbeat data; a sleep tracking device collectsdata relating to how much time a user/wearer spends sleeping; anutrition tracking device collects data relating to food and drinksconsumed by a user; a smart scale collects data relating to a bodyweight, body fat percentage, and/or body mass index (BMI), etc.Furthermore, a smart watch and/or smart phone, may be utilized as anactivity tracking device, a heart rate monitor, a sleep tracking device,and/or a nutrition tracking device.

The collected data is provided to one or more applications running onthe user device 110 in real time. The applications may includeheath-monitoring applications such as e.g., UA Record™, MapMyFitness®,MyFitnessPal®, Endomondo®, etc. each owned by assignee hereof. Otherhealth activity related monitoring applications may additionally beutilized in connection with the present disclosure, such as thosespecifically designed to receive information from a particular healthmonitoring device (i.e., an application which is published by the devicemanufacturer); the foregoing being merely representative of the generalconcepts of the present disclosure. In one embodiment, the user device110 reports the collected or sensed data to one or more user dataservers 102.

In one specific embodiment, a central database may be utilized such asthat described in co-owned, co-pending U.S. patent application Ser. No.15/002,036 filed on Jan. 20, 2016 and entitled “Methods and Apparatusfor Account Linking”, incorporated herein by reference in its entirety.As discussed therein, a plurality of applications running on a userdevice or in communication therewith are able to share data.Specifically, a single user device is configured to run a plurality ofheath-monitoring applications which collect data from a respectiveplurality of health-monitoring devices and/or via user entry; once theapplications are linked, the user accesses, views, and analyzes theplurality of health-related data from the plurality of applications at asingle application. In this manner, the user's activity and updatedinformation entered, sensed, or otherwise collected into or by oneapplication may be accessible at the other applications for analysis anddisplay therein as well.

Referring again to FIG. 1A, the user data servers 102 store thecollected/obtained/sensed data which is later accessed by an entityconfigured to generate a plurality of user profiles (e.g., therecommendation engine 104, curator apparatus 106, or other device). Theuser profiles include demographic and contact information relating toeach user including e.g., age, gender, mailing or billing address, emailaddress, etc. Additionally, the profiles may include aspects derivedfrom the collected health-parameter data including e.g.,activities/sports and/or celebrities of interest, weight, goals, etc.The user may be given an opportunity to confirm certain data in the userprofile which was derived or obtained from the collected data via anon-boarding process (discussed elsewhere herein); alternatively, theuser may manually enter certain data which cannot or is not derived fromother collected data. Once generated, the user profiles are stored at auser and product profile database 116 in one embodiment. Alternatively,the user profiles may be may be stored at separate database than theproduct profiles (see FIG. 1B below).

The user and product profile database 116 is further configured to storea plurality of product profiles. The product profiles may be generatedat e.g., the recommendation engine 104, or other entity in communicationwith the user and product profile database 116. In one embodiment, theproduct profiles are generated or extracted from a product catalogstored at the network 108. Each product profile comprises a table,matrix, or other plurality of records which describe aspects relating toindividual ones of the available products. In one specific embodiment,the number, p, of aspects listed in each product profile corresponds tothe number, m, of aspects of a user profile. In this manner, all of theproduct records may be filtered according to one or more user specificprofile aspects. Other mechanisms for record comparison will bediscussed in greater detail elsewhere herein. The results of thecomparison are then provided to a human curator at a curator device 106in the form of an ordered list of recommended products. Alternatively,only certain ones of the compared product records (i.e., those having athreshold level of similarity to the user profile aspects) are providedas recommended items to the curator. In another variant, the recommendeditems are provided to the curator in a sorted list by product category,such as tops, bottoms, shoes, accessories, etc.

Referring again to FIG. 1A, as noted above, the recommendation engine104 comprises a server or other computerized apparatus configured toreview an individual user profile against each of the available productprofiles in order to recommend one or more of the available productsbased on similarity and/or provide a list of all products in order ofmost to least likely to be of interest for purchase by the user. Therecommendation engine 104 is further configured to “learn” from at leastone feedback mechanism.

Specifically, as noted above, the recommendation engine 104 (or otherprofile generating entity) utilizes existing product data records togenerate or otherwise obtain (such as from another entity) a productprofile record for each available product. Additionally, therecommendation engine 104 (or other profile generating entity) pullsbasic user data (e.g., age, gender, weight, etc.), user activity data(including social media activity), and psychographic data from the userdata server(s) 102 and/or user devices 110. This data is utilized by therecommendation engine 104 (or other profile generating entity) togenerate a consumer profile record, which each user/consumer may confirmthe contents of and/or make updates to during an account set-up oron-boarding process.

As noted above, the recommendation engine 104 generates recommendations(i.e., selects one or more of a plurality of products targeted to aspecific consumer) by comparing each of the product records to theconsumer's profile via a comparison algorithm. These recommendations arethen presented to a curator or stylist at a curator apparatus 106. Thestylist selects from among the recommended products one or more itemsfor delivery the subscriber. Hence, a delivery order is created andprovided to the product warehouse 112 for fulfillment (i.e., shipment ofthe selected products to the user's location 114). As will be discussedin greater detail elsewhere herein, the aforementioned selection andshipment may, in one variant, be made periodically in connection with asubscription service (such as a monthly subscription service).

The subscriber, upon receiving the recommended and curator selectedproducts, selects which items to return and which to keep. Those itemswhich the subscriber is not interested in purchasing are provided backto the product warehouse 112, which then processes the return of thoseitems and the purchase of the items which were kept.

In one embodiment, the recommendation engine 104 (and/or otherappropriate service) is further configured to utilize at least onefeedback step to ensure that its recommendations are in line with theuser's preferences. A first feedback mechanism comprises utilization offeedback received from a human curator at a selection or curatorapparatus 106. As noted above, a list of recommended items are providedto the curator/stylist from the recommendation engine. When the humancurator or operator makes a selection of one or more of the items in thelist, information about the selection is provided back to therecommendation engine 104. In one variant, the curator may additionallyprovide detailed descriptive information relating to his/her reasoningfor selected/not selecting certain products. The feedback information isused by the recommendation engine 104 to adjust at least one of the userprofile, one or more product records, a comparison algorithm, and/or athreshold value for determining adequate similarity as discussedelsewhere herein.

A second feedback mechanism comprises utilization of feedback receivedfrom the user/subscriber. Specifically, as discussed herein, the itemsselected by the human curator are provided to a product distributionwarehouse 112 as a purchase order or delivery order. The productwarehouse 112 prepares and ships the selected products to the consumerlocation 114. Once the consumer receives the selected products, he orshe is given a specified period of time within which to return thoseitems the consumer does not want to purchase. In one variant, theconsumer may additionally provide detailed descriptive informationrelating to his/her reasoning for selected/not selecting certainproducts. The returned items are received at the product warehouse 112,which reports back to the curator apparatus 106 and/or therecommendation engine 104 information relating to the returned productsand/or the items which were not returned. The feedback information isused by the recommendation engine 104 to adjust at least one of the userprofile, one or more product records, the comparison algorithm, and/orthe threshold value as discussed below.

In a first embodiment, supposing an example in which feedbackinformation is received that a subscriber did not wish to purchaseProduct A, the subscriber's profile is updated. In one instance, aweighted product record is created by multiplying the product record forProduct A by a predetermined weighting factor corresponding to thereturn activity, then adding the weighted product record to the mostrecent profile for the user to create an updated user profile. Inanother instance, the specific aspects of user's profile whichcorrespond to the most influential or representative aspects of theproduct record for Product A are decreased by a value predetermined tocorrespond to the product return activity.

In another embodiment, the product record itself is adjusted. Accordingto this embodiment, the weighted product record is created, then suchproduct record is used in any subsequent comparisons of that product tothat or any other user's profile. In addition, other product recordsmeeting at threshold level of similarity to that of Product A may beidentified and likewise updated/weighted. Various mechanisms may be usedto determine similar products to Product A including, e.g., utilizingmetadata or other tags to the product records to identify a product orsport line, a filtering mechanism similar to that used to determinecloseness of products to a user profile, etc.

In yet another embodiment, the comparison algorithm itself may beupdated in response to the first and/or second feedback. In oneembodiment, this may include e.g., adding, subtracting, multiplying,dividing, or other performing any other mathematical operation with apredetermined value that corresponds to the return activity to thecomparison.

In a further embodiment, the similarity threshold may be adjusted inresponse to the first and/or second feedback. That is, rather thanrecommending products having a first similarity tolerance, therecommendation engine 104 may adjust the threshold to a number justabove the similarity level that was determined for the returned product,Product A. In the instance a plurality of products are returned, therecommendation engine 104 may use the lowest or highest similarity levelof all of the returned products to adjust the threshold in the manneroutlined above.

Additionally, the foregoing first and/or second feedback mechanisms maybe utilized by a computerized apparatus (e.g., the recommendation entity104) to identify generalizations within groups. For example, thefeedback information may indicate that women within the 18-25demographic have a higher rate of returning a particular pair of runningtights as compared to women in the 26-35 demographic. In anotherexample, it may be determined that people in the ZIP code 78701 maintaina consistent and high rate of keeping a particular pair of runningshorts irrespective of a sport seasonality thereof. Thesegeneralizations may then be applied by the recommendation engine 104 tofuture recommendations.

User-specific patterns may also be derived given the feedbackinformation. For example, it may be determined that for a particularuser any items which are brightly colored are kept whereas solid coloritems are kept less often. In another example, it may be determined thatthe user will not purchase anything priced above $50.

Patterns may further be derived from information relating to thecombination of the particular items that the curator sends and thoseitems which are kept or sent back by the subscriber. Specifically,patterns may be used to identify whether the curator selecting high/lowrated items (as recommended by the recommendation engine) and mayfurther give useful information relating to the rate of success of thecurator in these types of selections (highly vs. lower rated items).

As discussed above, the curator apparatus 106 may, in one embodiment, befurther configured to receive consumer feedback from the productwarehouse 112. Specifically, the curator apparatus 106 may utilize theconsumer feedback information to assist the curator him/herself inmaking future selections (i.e., human learning). Moreover, the humancurator may (via an interface at the curator apparatus 106) communicatedirectly to the consumer about his/her selections. It is further notedthat such interface and communication may be utilized by the curator tocommunicate with the consumer prior to making a selection of particularones of the recommended items as well. The consumer feedback informationis used by the recommendation engine 104 to adjust at least one of theuser profile, one or more product records, the comparison algorithm,and/or the threshold value in a manner similar to those discussed above.

Referring now to FIG. 1B, a detailed embodiment of an exemplary network150 for use with the present disclosure is shown. As illustrated,various services and entities are provided which cooperate with oneanother to provide the herein described functionality. Specifically, thenetwork 150 includes a recommendation engine 104 which utilizes one ormore algorithms for determining which of a plurality of products in aproduct database 156 are suitable for recommendation to a particularcustomer. The recommendations may additionally be effected by weatherconditions associated to a location of a customer obtained from theweather service 150. Alternatively, or in addition, the weatherconditions provided by the weather service 150 may be utilized by acurator at the curator interface 160 via the recommendation service 152.

A plurality of recommendation services 152 enable a curator to receivethe recommendations from the recommendation engine 104 and, in oneembodiment, enable the curator/stylist to select one or more items fromamong the recommended items as well as any other additional oralternative items to be delivered to the subscriber (as discussed ingreater detail below). The recommendations services 152 further enable aconsumer to access his/her account as is discussed in further detailelsewhere herein.

Once products are selected, an order for the products is generated andprovided to the product warehouse. The product warehouse is responsiblefor packaging and delivering the products to the subscriber. Items whichthe subscriber elects to keep are billed to the subscriber's account andthe product warehouse processes returns for the items which thesubscriber does not keep.

Information relating to those items which were/were not selected by thecurator/stylist and information relating to the items which thesubscriber kept/did not keep is provided, in one embodiment, to therecommendation engine 104 via the feedback service 154. Therecommendation engine uses the feedback information to enhance itsrecommendation algorithm.

As noted above, various recommendation services 152 are provided whichprovide the necessary infrastructure for enabling a subscription-basedautomatic delivery of recommended products, including, e.g., a customervalidation service, a payment authentication service, an order placementand management service, subscription management service, a scheduler,and an inventory search service. Additional services to provide theherein disclosed functionality which are not specifically referencedhere and which would be known to those of ordinary skill in the art mayalso be provided, the foregoing are merely exemplary of the generalconcepts of the disclosure.

The customer validation service comprises a means by which informationmay be obtained about a customer. For example, the customer validationservice may be configured to communicate with a customer database 158 inorder to obtain preliminary information about a customer when he/shebegins a set-up or onboarding process (discussed elsewhere herein). Inthis manner, information about the user may be preloaded to decrease theburden on the customer him/herself in having to manually enter theinformation. For example, it may be determined that the customer is afemale interested in running and yoga. In one embodiment, the customerdatabase may be of the type discussed in the previously referencedco-owned, co-pending U.S. patent application Ser. No. 15/002,036, whichenables information to be shared across multiple user accounts. Customeraddress and payment information may also be stored at the customerdatabase 158 and accessible by the customer validation service.Additionally, the customer validation service may validate a user'spassword and login credentials upon subsequent login attempts once theuser has completed the on-boarding process and established an account.

The payment authentication service enables a consumer's paymentinformation (whether entered manually or obtained from e.g., thecustomer database 158) to be verified. Payment authentication may beaccomplished via well-known credit card validation mechanisms, omittedfrom this discussion.

The order placement and management service comprises a means forenabling a customer to place an order (such as an order for asubscription service to begin), enabling a curator or stylist to orderspecific products from among the recommended products to be delivered tothe subscriber, generating order receipts based on the curator ordereditems, and adjusting orders according to user returns so as to properlybill the user only for those items which he/she keeps (does not return).

The subscription management service provides a means by which thesubscriber may make changes or updates to his/her subscription. Forexample, the subscriber may change his/her delivery preferencesincluding delivery frequency (e.g., weekly, monthly, bi-monthly, everysix months, etc.), and shipping address, etc. In addition, thesubscriber may be able to, via the subscription management service,access his/her style profile and make adjustments. In other words, asthe customer is on-boarding, a profile is created which reflects thecustomer's style inclinations (as discussed in greater detail elsewhereherein) and stored at the customer database 158. Later, the customermay, using the subscription management service, access this profile andenter updates. For example, the user may enter new sports of interest, apreference for a certain type of clothing (e.g., more basketballshorts), color and pattern preferences, etc.

The scheduler comprises a service by which timed events are managed. Forinstance, the curator/stylist may be notified of an upcoming deadline,such as a deadline to select a particular subscriber's recommendeditems. The scheduler also enables the subscriber to enter a schedule fordelivery of the recommended items and/or follows the deliveryinstructions set forth by the customer via the order management service(discussed above). The scheduler may also manage dates on whichshipments were sent and received, and keep track of a number of days inwhich the customer has to return any items from a recent delivery.

The inventory search service enables the curator to search the entireinventory of available products. The stylist may do so when he/shedetermines that the computer provided (i.e., recommendation engine 104provided) list of recommended products are insufficient. In this case,the stylist may find items which are similar to the recommended items,search for a specific item, and/or search using any number of filters.As will be discussed in greater detail below, the inventory searchservice may also be utilized in coordination with the recommendationengine to enable the recommendation engine to search the same inventoryfor a computer determination of recommended available products.

The customer interacts with the recommendation services 152 via userinterface 162. In one embodiment, the interface 162 is accessed by thecustomer via a user device 110 in communication with the recommendationservices 152 via a network 108. The interface 162 may comprise a webpage or website into which the customer enters in order to, inter alia,view or update his/her account, manage his subscription, scheduledelivery of his curated items, and communicate with his/her assignedstylist. Various interactions of the customer with the recommendationservices 152 further utilize a business to customer (B2C) backend 164,as discussed elsewhere herein.

Specifically, when a user first enters the site, an on-boarding processis initiated. In one variant, certain questions are presented to theconsumer which establish an account for the user. The answers to thequestions may be preloaded such as from information saved elsewhere(e.g., the customer database 158). Alternatively, or in addition, thecustomer may enter answers to individual ones of the questions manually.The questions may include gender, gender identity, types of exercise orsports in which the consumer participates, typical locations for thecustomer's activities or sports (e.g., outside, in a commercial gym, athome, etc.), individual style and/or fashion likes/dislikes, celebritiesand/or sports figures the customer follows or has interest in, sizes(tops, shoes, hats, bottoms, etc.), and so forth.

Once the account is created, the subscriber can log-in by providing apassword and user identifier combination. Upon validation of thepassword/user identifier combination, the user is provided with aninterface 162 to view his/her account details. The account details mayinclude saved payment methods, style profile (including sizes,preferences, etc.), assigned stylist, delivery schedule, deliveryaddress as well as a mechanism to view previously recommended/deliveredproducts, previously purchased products, and to communicate to theassigned stylist. The subscriber may further rate items and/or otherwiseprovide feedback relating to products which were previously delivered,as well as other products available for purchase.

In a further embodiment, one or more of the pages which a subscriber mayview comprises a display of information received from one or more linkedapplications. For example, data may be obtained at the user device 110relating to health parameter (e.g., activity, steps taken, sleep,calories burned and/or ingested, body weight, etc.); the device 110 mayrun a health-monitoring application which generates a display relatingto the collected user health parameter data. In one embodiment of thepresent disclosure, this display may be re-produced at the userinterface 162, such as via the apparatus and methods disclosed inpreviously referenced co-owned, co-pending U.S. patent application Ser.No. 15/002,036, which enables account linking. In such case, therecommendations services 152 are able to access the health-parameterdata at a linked database (such as the customer database 158).

The curator/stylist interacts with the recommendation services 152 viathe curator interface 160. In one embodiment, the interface 160 isaccessed by the curator via a curator apparatus 106 in communicationwith the recommendation services 152 via a network 108. The interfacemay comprise a web page or website into which the curator enters inorder to, inter alia, view or manage his/her list of customers, todirect ordering of recommended items for each consumer he/she curates,and communicate with his/her customers. Exemplary curator interface 160are illustrated at FIGS. 2A-2B and discussed below.

Similar to the subscriber set up discussed above, in one variant, eachstylist/curator must also establish a profile or account. The stylistprofile comprise publically reviewable information including e.g.,sports, activities, gender, a photograph, contact information, includinglinks to “friend” the stylist on certain social media sites, and/orother relevant information about the stylist. According to thisembodiment, the stylist also creates a password and user identifiercombination for log-on. Upon validation of the password/user identifiercombination, the stylist is provided with an interface 160 to viewhis/her account details. In one variant, the stylist is first brought toa task page which lists any outstanding tasks to be completed by thestylist. For example, the task list may indicate one or more subscribersfor which selections from among the computer recommendations are neededso that delivery thereof may be completed. In another example, on thetask list may indicate one or more selected items for delivery are nolonger in stock or available. The task list may further indicate thestylist's progress on certain ones of the tasks on the list, e.g.,recommendations begun/incomplete.

Exemplary Interface

Referring now to FIG. 2A, an exemplary curator interface 200 forenabling the curator to view computer generated recommendations isgiven. As shown, the interface generally comprises a first panel whichlists the computer-generated recommendations (i.e., generated at therecommendation engine 104) and a second panel which gives subscriberdetails.

Specifically, the first panel (shown on the left portion of FIG. 2A) ofthe illustrated interface 200 includes a plurality of sections relatingto computer-generated (i.e., recommendation engine 104 generated)recommendations, including: a “Recommended Tops” section 202, a“Recommended Bottoms” section 204, a “Recommended Shoes” section 206,and a “Recommended Accessories” section 208. Additional, otherrecommendation-related sections may be provided, the foregoing beingmerely exemplary of the general concepts of the present disclosure.

Each of the foregoing sections 202, 204, 206, 208 comprises a scrollablelist of products (e.g., tops, bottoms, shoes, and accessories,respectively) selected by the recommendation engine 104 as being relatedto a subscriber's profile. In one embodiment, every product is listed inorder of similarity or relevance to the subscriber's profile.Alternatively, only a given number of the top or best matches areprovided in the scrollable list. As illustrated by the check mark inFIG. 2A, the curator/stylist is able to scroll through the recommendedtops 202 in order to find one or more which are selected in order to beadded to a “box” to be delivered to the subscriber. In the illustratedembodiment, a score for each item is provided along with a shortdescription, price, and image. Score calculations will be discussedelsewhere herein.

In one embodiment, one or more rules may be applied to the curator'sselections via a rules engine (not shown). For example, the interface200 may limit the total number of items which may be selected. Inanother alternative, the limit may be placed on the sections, e.g., someset number of tops, bottoms, shoes, and accessories are required. Inaddition, the rules may have a temporal requirement, for example,running shoes are only permitted for inclusion in a subscriber's boxwhen more than 2 months have passed since the last running shoe purchaseby the subscriber. Other rules which set a minimum and/or maximum totalvalue for a curator's selections and/or a per item minimum and/ormaximum may also be given. Any combination and required number of itemsmay be set in the rules applied at the interface 200.

The scrollable sections 202, 204, 206, 208 may be further filtered via adropdown filter menu 226. In the illustrated embodiment, the recommendedbottoms 204 may be filtered by e.g., leggings, prints, running, joggers,and lifestyle. It is further appreciated that the recommended tops 202may be filtered by sports bras, sleeveless, long sleeved, t-shirt,compression, etc. Recommended shoes 206 may be filtered by e.g.,running, cross training, hiking, boots, casual, colors, etc.;recommended accessories 208 may be filtered by e.g., hats, headbands,socks, bags, etc.

The second panel (shown on the right portion of FIG. 2A) includes aplurality of sections relating to the athlete/consumer (e.g.,subscriber) him/herself, including: a general customer informationwindow 212, various windows within a subscriber profile tab 228.Additional, other subscriber-related sections may be provided, theforegoing being merely exemplary of the general concepts of the presentdisclosure.

The general customer information window 212 provides a minimal amount ofinformation for the stylist to recognize the consumer. In the givenexample, the stylist is provided with the subscriber's name, gender, andgeneralized geographic location (e.g., Sophia, female, Austin, Tex.).Below the general customer information window 212 are a profile tab 228and a box history tab 230. The contents of the profile tab 228 areillustrated in FIG. 2A, the contents of the box history tab 230 areillustrated in FIG. 2B (discussed below).

The subscriber profile tab 228 includes a notes window 214, a climateindication window 216, a customer/athlete profile window 218, acustomer/athlete preferences window 220, and a customer/athletesizing/measurement window 222. The notes window 214 comprises a windowin which the stylist may enter and save text and/or image notes relatingto interactions with the consumer, noted patterns or preferences of theconsumer, etc. The climate indication window 216 illustrates to thestylist the typical weather for the geographic location of thesubscriber. In the given example, for the month of September typicallythe weather ranges between 70-91° F. in Austin, Tex. Additionally, it isillustrated that in Austin, Tex. in September there is typically 1 dayin 30 in which it rains. Although, climate/weather is taken intoaccount, in one embodiment, by the recommendation engine 104 when therecommended products are selected (see discussion below), it isappreciated that the stylist may further consider this information whenselecting products from among the computer-selected items.

The customer/athlete profile window 218 lists basic aspects relating tothe subscriber including e.g., the name, gender, birthday, emailaddress, phone number, and connected applications (such as UA Record™,MapMyFitness®, MyFitnessPal®, Endomondo®, etc.). The customer/athletepreferences window 220 provides information relating to variousactivities, styles, etc. which may assist the curator in selectingproducts. In the illustrated example, the specific preferences includee.g. activities, environment (such as gym workouts, outdoor workouts,etc.), a style identifier (e.g., classic, progressive, edgy, etc.),inspirational celebrities/athletes, and frequency (e.g., monthly, everythree months, every six months, etc.). Finally, the customer/athletesizing/measurement window 222 provides the curator with specificinformation relating to the subscriber's specific sizing information. Inone embodiment, a subscriber's specific measurements may be provided.Alternatively, the user's general sizing information may be provided (asillustrated). Specifically, in the embodiment of FIG. 2A, thesubscriber's height, weight, chest, bust, cup, waist, inseam, shoe andhead sizes are listed. In a further embodiment, as discussed in greaterdetail below, changes to a user's sizes may be made by the curator inresponse to reported weight loss goals being met (and recorded in aconnected application).

Each of the foregoing windows 214, 216, 218, 220, and 222 may furtherprovide alternative or additional information (not illustrated); theillustrated embodiment being merely exemplary of the general concepts ofthe disclosure.

Lastly, the exemplary interface 200 of FIG. 2A includes an order summarywindow 224 (shown on the bottom left portion of FIG. 2A). The ordersummary window 224 provides a summary of the products selected by thecurator as they are added to the subscriber's box, a running total pricefor the items in the subscriber's box, and a window within which thecurator may leave a personalized message to the subscriber regarding thecontents of the box, such as why certain items were selected, etc. Oncethe curator has selected a sufficient number and type of items for thesubscriber's box (as determined by the aforementioned rules), andentered a personalized message for the subscriber, the curator uses the“submit order” button 225 to cause a delivery order to be generated andprovided to the product warehouse 112 for fulfillment. In addition, anelectronic message may be sent to the subscriber including the contentsof the order summary window 224.

Referring now to FIG. 2B, a drop down menu 252 which providescomputer-generated scores on which product matching is performed isillustrated. As shown, the item of FIG. 2B was determined to have anoverall score of 37%, which is comprised of several score modulesincluding in this example, an 87% popularity score, a 47% weathermatching score, a 12% sports seasonality score, and a 2% profile match.The popularity, weather, sports seasonality and profile match scores arederived by the recommendation engine 104 as discussed elsewhere herein.A visual representation of the individual score modules may be providedsuch as e.g., using colors, a bar graph, etc. When determining theoverall score for the product and in selecting certain products fordisplay and/or a priority or hierarchy thereof, the recommendationengine 104 applies a weighting scheme to each of the foregoing scoremodules. For example, the weighting scheme may provide that e.g.,profile matches are weighted at 4×, sports seasonality and popularityare weighted at 2×, and weather matching is weighted at 3×; otherweighting schemes may also be applied, the foregoing being merelyexemplary. Additionally, the weighting schemes may be dynamicallymodulated or modified based on feedback information from e.g., thestylist and/or customer. In another embodiment or alternative, weightingmay be performed on a per-subscriber basis. For example, it may be notedthat for a particular user, price is the most important factor indetermining whether an item will be kept. Hence, price may comprise aseparate score module which is highly weighted (e.g., weighted at 5×).Still further, each demographic may have a predetermined set of weightswhich are applied to the score modules discussed herein. A detaileddiscussion of the score module and sub-module calculations and weightingschemes will be discussed in greater detail elsewhere herein.

Additionally, FIG. 2B illustrates the exemplary customer/athlete historypanel 254 which is displayed to the curator when he/she selects the boxhistory tab 230. As shown, the history panel provides a per box summaryof each item provided to the subscriber. The summary may indicate totalprice for the box, price for only the items which were purchased fromthe box, the item descriptions and images, whether each item was kept orreturned, as well as reasons for the subscriber's decision to keep or toreturn the item. The reasons for returning and/or keeping the items mayinclude a description of the subscriber's feeling for the size, fit,colors, and style of the items, as well as score in a rating system(e.g., 4 out of 5, stars, etc.). Finally, the consumer may provide textnotes and/or uploaded images relating to the product.

In another specific implementation, data relating to the user's physicalactivity data, nutrition, and/or social networking may be obtained frome.g., one or more user data servers 102, user devices 110, customerdatabase 158, and/or other entities configured to store such information(e.g., the previously referenced databases described in co-owned,co-pending U.S. patent application Ser. No. 15/002,036). The datarelating to the user's physical activity, nutrition, and/or socialnetworking is displayed to the curator (not shown). In this manner, thecurator may review the user's most recent patterns of behavior in orderto make determinations relating to the curated items. For example, thecurator may identify that the user's activity of running generally takesplace in the evenings, which are generally colder than the averagetemperatures for a geographic region which are used by therecommendation engine (noted elsewhere herein). Hence, the curator mayselect warmer clothing items, hats and/or jackets which might not havebeen as highly recommended by the recommendation engine.

Exemplary Methodology

FIGS. 3A-3F below describe methods which utilize the foregoing network100 and/or 150 to enable enhanced recommendations in accordance with thepresent disclosure.

Referring now to FIG. 3A, a generalized method 300 for enabling asubscriber to create a subscriber account (referred to herein as“on-boarding”) is illustrated. As shown, per step 302, the consumer isprovided with a subscriber acquisition page. In one embodiment, thesubscriber acquisition page comprises a web-based page or applicationscreen which invites a customer to sign up for enhanced recommendationservices. The subscriber acquisition page is provided on a userinterface 162 using e.g., B2C backend services 164 in coordination withrecommendations services 152. In one variant, the enhancedrecommendation services may comprise a subscription service forperiodically providing customers with one or more curator selectedproducts from among a plurality of computer recommended items.Alternatively, the recommendations services may create virtual boxesfrom which the customer him/herself may select items for purchase.

Next, per step 304, it is determined whether the customer is able tosubscribe to the recommendation service. For example, a customer may notbe able to sign up for the service if there is no available curator towhich the customer may be assigned (i.e., the current demand is higherthan the current number of curators are able to service). In this case,the customer will be placed on a waiting list (step 306) until there issufficient bandwidth to service their account. In another example, aparticular customer may not be able to sign up for the service if it isdetermined that the user has already enrolled in the program. In thiscase, the user may be redirected to an account log-in page (step 306).In one exemplary embodiment, the determination at step 304 isaccomplished via the recommendations services 152 performing a search ofthe current customer database 158 for information identifying thewould-be subscriber to determine whether the customer has alreadyestablished an account. Additionally, or in the alternative, therecommendation services 152 may search one or more stylist backlog liststo determine whether there are any stylists available and/or determinewhether a predetermined number of subscriber slots that can beaccommodated have already been filed.

When it is determined that the user is able to subscribe to therecommendation service (at step 304), then the method 300 proceeds tostep 308 where the customer is provided with one or more profilecreation pages. The profile creation pages may comprise web-based orapplication pages which ask the customer a series of questions relatingto e.g., activities, preferences, demographics, etc. of the customer.The profile creation pages are provided via coordination betweenrecommendation services 152 and the B2C backend 164. In one variant,answers to certain ones of the questions may be pre-loaded into thepages. For example, the recommendations services 152 may pull datarelating to the customer and his/her activity from the customer database158 and/or user data servers 102. In this manner, the page may pre-loade.g., that the customer is a female, interested in yoga and running, andfollows the University of Maryland Terrapins college football team. Thecustomer may verify and/or modify the preloaded information and/orprovide additional answers/information at the creation pages of step308.

Once the profile creation page(s) have been adequately filled in, perstep 310, an account submission/checkout page is provided. In otherwords, the account creation may include a step for the customer to entera frequency for receiving a box of curator selected and computerrecommended products. Accordingly, payment information is collected andother information required for processing payment (e.g., billingaddress, delivery address, etc.) at step 310 and the customer is invitedto review his/her order prior to its submission. The customer validationservice, subscription management service, scheduler service, and paymentauthentication service of the recommendation services 152 may each beutilized to facilitate accounts submission and checkout. The customerconfirms the order and details, and the order is placed at step 312. Toconfirm the subscription request, an order confirmation page ispresented to the customer at step 316. The order confirmation page mayprovide certain details relating to the order including e.g., aconfirmation number, account number, profile name and other detailsrelating to the customer and/or payment method. The method continues atstep 318 to the subscription flow wherein various services of therecommendation services 152 perform steps necessary to enable thesubscription (e.g., the subscription management service, the schedulerservice, and the order placement/management services).

FIG. 3B illustrates an exemplary subscription flow method 318. As shown,per step 320, a curator is assigned to the new customer/subscriber. Thismay be accomplished, in one embodiment, by a scheduler, subscriptionmanagement, or other service at the recommendation services 152 matchingcertain user profile details to those of the curator (e.g., gender,sports/activities, etc.). Alternatively, a curator may be assigned basedon availability, current workload, or a simple next curator in lineprocess. As noted above, rules may be provided for how many activesubscribers each curator may be assigned.

Next, per step 322 a profile framework is created using e.g., theinformation entered by the customer during the on-boarding process. Theprofile framework is utilized to create an account landing page intowhich the customer may log-in and modify as necessary (step 324) viacoordination between the B2C backend 164 and recommendation services152. For example, during the onboarding process the customer may haveomitted to enter certain information, the profile framework is set upand the information may be highlighted as requiring userinput/update/modifications. In other words, the profile framework mayset a placeholder for unknown information. A profile framework may alsobe useful when a subscriber has started, but not completed, his or heron-boarding process. In addition, if not performed previously, thecustomer's payment method is authenticated at step 326 via a paymentauthentication service at the recommendation service 152 backend.

Once payment by the subscriber is authorized, per step 328, thesubscriber is added to the assigned stylist/curator's queue (via thesubscription management and/or scheduler service) and recommendationsare generated via step 330.

Referring now to FIG. 3C, a method 330 for providing enhancedcomputer-recommendation of products is given. As shown, per step 332,product data and inventory information is collected. In one embodiment,this data/information may be collected by the recommendation engine 104from e.g., the product database 156 and/or the user and product profiledatabases 116. At step 334 customer data is collected by therecommendation engine 104 from e.g., the customer database 158 and/orthe user and product profile databases 116. As noted elsewhere herein,the collected product data may include previously generated data recordsrelating to the products which are placed in a format similar toconsumer profile data records; in this manner, the product and consumerrecords may be easily compared. As also discussed elsewhere herein, thecustomer profile or data records may be prepopulated using informationobtained about the customer from e.g., other linked applications, suchas health-parameter monitoring applications and/or via the user dataservers 102. In addition, the customer profile records may be updatedperiodically and/or consistent with changes or updates by the customerat the linked applications.

Various comparison calculations are provided at steps 336-342, it isappreciated that the listed comparison calculations may be performed inany order and/or additional or other comparisons may be included aswell, the foregoing being merely exemplary. In other words, the scoremodules discussed herein (steps 336-342) are modular such that scoremodules may be added and/or removed as needed. Additional modules mayinclude, e.g., a popularity of products among curators, a multiple itemscore (i.e., which items are more likely to be purchased together), itemprice, combined shipment price, etc. For example, a module may beprovided which takes into consideration reviews of products (starratings and/or natural language processing of text reviews) fromecommerce or online purchases. Another module may account for popularityand/or product rate of return or reviews at non-online retailers (i.e.,so called “brick and mortar” stores). Finally, a module may be providedwhich is able to draw conclusions about a user's overall health from aplurality of collected data, including data related to e.g., sleep,activity, fitness, nutrition, etc. In addition, the herein discussedcalculations and comparisons are performed at the recommendation engine104 or an apparatus in communication therewith in one exemplaryembodiment.

In the illustrated embodiment, at step 336, the product data is examinedfor popularity to determine an overall popularity of each product. Theproduct popularity may be displayed to the curator (as in the embodimentof FIG. 2B) and/or may be used in determining an overall recommendationscore of the product. The information needed to provide this comparisonor calculation may be obtained from data generated by the productwarehouse 112 relating to returns and stored or processed at e.g., thefeedback service 154. In one variant, the examination may include adetermination of the probability that the particular item when deliveredin connection with a subscription service will be purchased (i.e., notreturned). This examination may be further dissected or filtered by timeof year, gender, age, geographic location, etc. For example, it may bedetermined that females over 45 who receive Product A have a 67%probability of returning the item.

In another variant, popularity (at step 336) may be determined based oninventory data. That is, the recommendation engine 104 (or other entityin communication therewith) is configured to determine a number of daysa product has been available for purchase, and a corresponding relativedemand for the product over a time period, e.g., in the last month, inits first month of availability versus the current month's sales, inorder to predict a popularity score for the product. The popularityscore may also be provided to the curator as illustrated in FIG. 2B.

In yet another variant, popularity may be made per-quarter such thatitems which are determined by the business to be “key items” (i.e.,heavily promoted items) are weighted more heavily. Alternatively or inaddition, items for which there exists a higher than average inventorymay be weighted more highly in an effort to sell these excess items.

In another variant, popularity may utilize a Naïve Bayes model todetermine probability a purchase will be made given a productsparticular attributes and past transactions of this or other users. Inthe herein provided attributes may include a set of productfeatures/tags associated with each item, e.g., football, loose fit, allseason, etc. The particular user's affinity for a given set ofattributes may be cross referenced to the product features.Alternatively, it may be derived that a particular user is more likelyto have purchases which align to those of other, similarly situatedusers. The similarity of this user to others may be determined based ondemographics, past purchases, profile similarity, etc.

At step 338, the product data is compared to weather/climate data todetermine an appropriateness of each product to the weather/climatewhere the consumer will make use of the product. In one embodiment, thisis performed at the recommendation engine 104 which first determines ageographic location of the customer and then accesses data from aweather service 150 for that geographic location. In one variant, thegeographic location of the customer is determined based on a zip codeentered with the user's billing address or mailing address and accessedfrom customer data, records or a profile stored at the customer database158, user and product profile databases 116, and/or user data servers102. In another variant, a global positioning system (GPS) within theuser device 110 or internet protocol (IP) address may be accessed inorder to determine a current physical location. Next, the weather datais obtained from an appropriate data source, the weather data mayinclude e.g., average temperatures and average precipitation levels.Finally, the weather data for the particular geographic location iscompared against the product data. In order to accomplish thiscomparison, in one embodiment, the product data may include climateand/or weather appropriateness identifiers. For example, a particularproduct may be rated for rain (i.e., is waterproof), or cold weather(i.e., has long sleeves and/or other warmth retention features). Inanother variant, the weather data which is taken into consideration maycomprise “look ahead” data. That is, the weather may be reviewed forfuture periods (e.g., one month ahead) so as to take into account theremaining time from order submission and delivery of the product to thecustomer. For each available product, a separate score may be derivedrelating to each of temperature and precipitation.

At step 340, data relating to consumer's specific preferences iscompared to the product data to determine a likelihood that the consumerwill prefer a style, fit, etc. of each product. To accomplish thiscomparison, the recommendation engine 104 obtains (i) product datarecords from the product database 156 and/or the user and productprofile databases 116, and (ii) subscriber data records (e.g., profilerecords) from the customer database 158, the user and product profiledatabases 116 and/or the user data servers 102. The data is compared inorder to determine a percent match for a given item to a specificsubscriber profile.

In one specific embodiment, a first set of aspects are set as primaryfilters; such filters include gender and size (e.g., shoe size, topsize, bottom size, etc.). As discussed elsewhere herein, these aspectsare relatively static in nature and therefore, products which are notintended for a particular user's size or gender will not be provided tothe curator as recommended products. Hence, the products/items which donot meet the first set of aspects relating to the particular subscriberare no longer evaluated (i.e., are filtered out) for subsequentcomparison and recommendation purposes.

A second set of aspects are utilized to derive a score, such aspects aremore dynamic in nature and products may be ranked according to theirapplicability to the given aspect. For example, sports/activities inwhich the customer participates, style and color preferences, etc. maybe utilized in a comparison to the product data. Products which closelymatch the user's profile preferences will be scored more highly.

At step 342, the product seasonality is rated for applicability to thecurrent season (or the season during which the product will be deliveredto the subscriber). In one embodiment, the applicability is determinedby examining the product profile data against a calendar date on whichdelivery is scheduled and a predetermined range for each sport category.In other words, the recommendation engine 104 determines whether thesports season to which the product is related is within a predeterminedrange of the scheduled delivery date. For example, if a product isparticular to basketball, and the delivery date of the product is notexpected to be within an acceptable range of dates or months in whichbasketball-related products are normally purchased, the product is givena low sport seasonality rating. The system may further incorporatespecific dates of interest relating to one or more sports (e.g.,Olympics, World Series, NBA Finals, Super-Bowl, All-Star games, etc.).These specific dates and date ranges may be cross-referenced in oneembodiment to sports teams which are near a particular subscribergeographically in order to determine not only that the NBA Championshipgame is occurring soon, but also the local team is participating in thebig game. Additionally, the user's specific preference for a givensport/activity may be taken into account (e.g., an interest inbasketball generally, an interest in specific players or teams, activitydata logged for a given sport, etc.).

Each of the aforementioned comparison basis (e.g., popularity, weatherappropriateness, style match, and sport seasonality) or score modulesare, in one embodiment, used as the basis for determining an overallscore for the product as it relates to the particular subscriber and/ora group of subscribers. The overall score is provided to the curator ate.g., the curator interface 250. In addition, the aforementioned scoremodules (e.g., popularity, weather appropriateness, style match, andsport seasonality) may be individually provided for viewing by thecurator such as in a drop down menu 252 on the curator interface 250. Inthis manner, the curator may see a visual representation of why aproduct is/is not highly recommended in order to assist in selectingcertain ones thereof for inclusion in the subscriber's next delivery. Aswill be discussed in greater detail below, each score module is weightedaccording to its ability to precisely identify appropriate products.

Optionally, per step 344 of the recommendation flow 330, therecommendation engine 104 makes one or more adjustments to its productrecommendations on the basis of feedback information received from thefeedback service 154. The feedback information utilized may includeinformation which originated at the curator apparatus 106 and whichrelates to items which the curator selected/did not select for deliveryto the subscriber. In addition or alternatively, the feedbackinformation utilized may include information which originated at theuser device 110 and/or the product warehouse 112 and which relates toitems which the subscriber returned/did not return from the curated boxof recommended items. The feedback information may be utilized to derivepatterns which assist in future recommendations. For example, therecommendation engine 104 may determine one or more items which worktogether (i.e., which when sent together are more likely to bepurchased); additionally, the recommendation engine 104 may identifycommon attributes of returned items and common attributes of stylistselections on a per subscriber basis.

In one embodiment, the feedback information is used by therecommendation engine 104 to adjust at least one of the user profile,one or more product records, a comparison algorithm, and/or a thresholdvalue as discussed elsewhere herein. In another embodiment, the feedbackinformation may be used to adjust a scores of one or more of theabove-referenced score-modules.

As noted above, the overall score for a given product is calculated bythe recommendation engine 104 at step 346. Specifically, therecommendation engine 104 weighs the result of each of theaforementioned score modules (steps 336-342) according to the ability ofeach to predict a fitness of the product to the subscriber's profileand/or a likelihood of purchase of the product by the subscriber. Theaforementioned feedback information may be weighted prior to beingapplied to individual ones of the scores (as noted above), or may betaken into account prior to the weighting at step 346. In anotheralternative, the feedback information may be taken into considerationafter the weighting occurs at step 346. In one embodiment, the weightingfactor for each score module is manually entered by an operator at therecommendation engine 104. Over time, the recommendation engine 104 may“learn” optimum weighting factors on a per subscriber, per curatorand/or per item basis, such that these may be adjusted automatically andwithout substantial operator intervention. This may be accomplished viamanual entry followed by automatic adjustments based on e.g., theaforementioned feedback information. In another variant, regressiontechniques (e.g., logistic regression) may be applied to optimize theweights based on whether or not products are kept byconsumers/subscribers.

The weighted scores are then added together in one embodiment togenerate a total score per product. The products are then organizedaccording to their total score and displayed to the curator asrecommendations. In one embodiment, only those products having a scorewhich meets a predetermined threshold will be displayed to the curator.Alternatively, all products will be displayed in order by scoreirrespective of the score thereof. In either embodiment, the productswhich were filtered out will not be displayed to the curatorirrespective of score or display type.

In one particular example, the operator at the recommendation engine 104may set the score modules to be weighted as follows: profile scoremodule (e.g., subscriber's dynamic aspects) is weighted at 4×, thetemperature score module is weighted at 3×, the sports seasonality scoremodule is weighted at 2×, the popularity score module is weighted at 2×,and the precipitation score module is weighted at 1×. In anothervariant, a further distinction may be made within the category of sportseasonality so as to account for a product which fits more than onesport group. For example, each item may be given primary and secondarysport associations which are weighted differently. The primary sportassociation comprises the main sport to which the product is associatedand may be weighted higher than secondary sport associations. In onevariant secondary sport associations comprise a list of additionalsports to which the product may be associated; these may be weightedless than the primary sport associations and may be representative of anaverage of all of the secondary sports to which the product isassociated. As used herein, the term “sport” may refer to any activityincluding but not limited to individual sports, team sports, athleisure,etc.

As discussed elsewhere herein, the specific amount by which each scoremodule is weighted may be dynamically modulated or adjusted based onfeedback from e.g., the stylist and/or the customer. For example, it maybe determined that items recommended by the recommendation engine areinfrequently selected by the curator and/or infrequently kept by theuser in favor of products which have a strongly alternate profile thanthe computer recommended items. In one specific example, suppose arecommendation engine determines that items which are appropriate for awarmer climate should be provided to a set of users. However, it isdetermined that those users frequently return the items and insteadelect to keep colder weather gear. It may then be concluded that theweighting of the weather score module is too high and should beadjusted.

In another embodiment, the foregoing weighting may be adjustedper-subscriber and/or per-demographic. In this manner, as a subscribermake selections to keep or return items, this information is utilized toadjust what score modules are more important to that subscriber. Thesystem may start each subscriber or subscribers within a givendemographic with a particular set of weights applied to score modules,which are then adjusted over time as the subscribers provide feedback.Similar logic applies to a demographic groupings such that adjustmentsare made to a wider population of subscribers within a demographic.

After the weights are applied and scores are determined forrecommendations (step 346), the recommended products are presented on acurator interface 200 and 250 and the curator is, per step 348 provideda means to select one or more of the recommended products for deliveryto the subscriber. The curator selection of particular products, asnoted above, may be based on the recommendation engine 104 generatedscores. In addition, the curator may take into account his/her ownobservations and motivations when selecting items for delivery.

As also noted elsewhere herein, the curator may be required to meetcertain requirements or rules when finalizing a curated box fordelivery. For example, a certain minimum and/or maximum numbers of itemsmust be selected, a number of each type of item may be required, amaximum of each type of item may be provided, a minimum and/or maximumtotal price may also be required to be met, and/or the system may alsoidentify products which are set to be discounted within a set timeperiod and set a rule that these products should not be shipped afterthe time period. For example, if an item will be discounted on May1^(st), the rule will disallow it to be added to a shipment which is setto be delivered less than 45 days before May 1^(st).

Once the curator has selected appropriate numbers and types of items, anorder for the items is provided to the product warehouse 112 and thefulfillment flow step 350 is entered. Additionally, an optional feedbackflow is entered at step 368 which relates to the curator's selections(at step 348). Each of these flows (350 and 368) will be discussed infurther detail below.

Referring now to FIG. 3D, a logical flow diagram illustrating ageneralized method 350 for fulfilling a curator-selected order ofcomputer-recommended products is given. As shown, per step 352 an orderis generated. The order comprises a computerized listing of those itemswhich were selected by the curator at the curator interface 200 and 250.In addition, the order lists the customer contact information necessaryfor delivery of the items thereto (e.g., name and address); a customeridentifier and/or payment method may also be listed to ease theprocessing of returns (as discussed below). Customer information,including name, address, identifier, and/or payment method may beaccessed by the curator via the customer validation service and/orpayment authentication service provided via recommendation services 152and/or information obtained from the customer database 158, user andproduct profile database 116, and/or the user data servers 102. Theorder is provided from the curator apparatus 106 to the productwarehouse 112 at step 354.

When the order is received at the product warehouse 112, it is processedfor packaging and shipping to the subscriber (step 356). In addition,shipping materials and a return form which includes the customeridentifier may also be shipped in order to ease the return process (asdiscussed below).

The customer/subscriber is given a predetermined amount of time in whichreturns of the items in the delivery may be processed. Hence, per step358, the process 350 proceeds to wait for the predetermined period. Atstep 360, it is determined whether any of the products were returnedwithin the return window. Items which are not returned within the returnwindow (either because they were purposely kept or because the timewindow expired) are per step 362 processed for payment. In oneembodiment, the payment is processed via the product warehouse accessinga payment method which was provided by the curator in the order (seediscussion of step 352 above), or pulled from e.g., the customerprofile. Items which are returned within the return window are, per step364, processed using e.g., traditional return mechanisms. That is, theitem is inspected, repackaged, and placed back into inventory orcirculation, and the customer is not charged for the item.

Finally, at step 368, the feedback flow is entered which relates to thesubscriber's elections to keep or return items.

The feedback flow 368 is illustrated at FIG. 3E. As shown, per step 370,a feedback page is provided to the curator and/or subscriber. In oneembodiment, the feedback page may take the form of a web-based interfaceinto which the curator and/or subscriber may enter information.Alternatively the feedback page may comprise a physical paper on whichthe curator and/or subscriber enters comments, such as e.g., paperworkprovided with a shipment to the subscriber. In either instance, thecurator and/or subscriber is able to enter feedback into the page (suchas by typing, selecting options, and/or writing). In the instance thefeedback page comprises a web page, the page may be pre-loaded with theitems which were delivered to the subscriber and the entry of feedbackmay comprise enabling the subscriber to comment regarding the fit,style, etc., of each item and/or to select one or more designatorsrelating to satisfaction (e.g., four out of five stars, 8 out of 10,etc.). The feedback may request a level of detail which best enables thecurator and/or the recommendation engine 104 to make use of theinformation. It is appreciated that similar webpages may be provided tothe curator to enable the curator to provide information regarding aselection or dismissal of certain items which were recommended by therecommendation engine 104. In the instance the feedback page comprises aphysical paper, the paper may list each item which was delivered to thesubscriber, and the subscriber hand writes comments and/or marksindicators for each item provided thereto (e.g., fit was too small, toolarge, just right, etc.). The subscriber may further be required to handwrite the item names or descriptions in another embodiment.Predetermined alpha-numeric codes may be provided to enable consistentprocessing.

Data relating to the entered feedback is provided to the feedbackservice 154 at step 374. In one embodiment, the raw data entered by thecurator and/or subscriber is processed into a format which the feedbackservice 154 is able to read. For example, text and hand writtenresponses may be formatted using a word search feature to arrive at ageneral response category into which the comment may be placed.

The feedback service 154 uses the feedback data to modify the customer'ssubscription (step 376) and/or the customer's profile (step 378) whereappropriate. Specifically, when the feedback indicates that the customeris unable to purchase items because he/she doesn't have enough budgetedfor the expense, the customer's subscription may be modified to decreasethe number of curated boxes the customer will receive and/or adjust therules relating to minimum and/or maximum costs. In another example, thecustomer may consistently report a size small top as being too small. Inthis case, after a predetermined number of returns for this reason, thecustomer's profile may reflect that he/she is in fact a medium and not asmall.

It is further appreciated that certain feedback may be utilized by thefeedback service 154 to immediately effect the next recommendationand/or to cause a mini-delivery to be shipped to the customer.Specifically, when feedback data is returned that the customer gave aproduct highest ratings in style, color, etc., but that the sizeprovided was too small or too large, the feedback service 154 mayindicate to the curator device 106 that he/she may want to follow upwith the customer to determine whether the customer would prefer toreceive the item again but at a new size. Alternatively, an order forthe changed size may be generated by the feedback service automatically,and optionally sent to the curator for approval, or providedautomatically to the product warehouse 112 for fulfillment.

Additionally, the threshold values, weighting variables, and/oralgorithms used to determine product recommendations by therecommendation engine 104 may be adjusted based on the feedback data(not shown).

Referring now to FIG. 3F, a generalized method 380 for providingenhanced product recommendations in accordance with one embodiment ofthe present disclosure is provided. As shown, per steps 382 and 384,product profiles and user profiles are obtained from e.g., the user andproduct profile database 116. As noted elsewhere herein, the product anduser profiles may comprise vectors, matrixes, or tables. The userprofiles may be generated from information obtained about the user frome.g., a user data server 102, and the product profiles may be pulleddirectly from one or more inventory catalogs. Moreover, a separatecustomer database 158 and product database 156 may be provided forstorage of the user and product profiles, respectively, in anotherembodiment.

Next, per step 386, the product profiles and user profiles are comparedto arrive at one or more product recommendations (step 388), i.e.,products which have a threshold level of similarity to the user profile,which are provided to a curator apparatus. As discussed elsewhereherein, the comparison of the product and user profiles may be performedvia utilization of a filtering algorithm and the remaining productsevaluated within the context of one or more scoring modules. Theresultant scores are then weighted and totaled across each of thescoring modules to arrive at an overall score, which is used by therecommendation engine 104 to identify one or more products to beprovided as selectable options to the curator (step 388).

The curator (a human operator at the curator apparatus 106) selectscertain ones of the recommended items and based on the selection, afirst round of feedback is received at the recommendation engine 104(step 390). This curator-selection based feedback is used to adjust therecommendation at step 392.

Meanwhile, the curator-selected ones of the recommended products areprovided to the subscriber. The customer may elect to purchase fromamong the provided items. The ones of the provided items which theconsumer does not elect to purchase are returned. Information regardingthe returned items and/or items kept for purchase by the consumer isprovided as second round feedback to the recommendation engine 104and/or curator 106 at step 394. This subscriber-selection based feedbackis also used to adjust the recommendation at step 396. Specifically,both the recommendation engine 106 and/or the curator 106 him/herselfmay use the second round feedback to make adjustments.

The process 380 repeats at step 386 where product profiles are againcompared to the user profile after a predetermined period of time. Forexample, the foregoing method 380 may be utilized in a subscriptionservice, such as where the user/consumer receives selected itemsperiodically, e.g., monthly, every 8 weeks, etc.

Exemplary Workflow

FIG. 4 illustrates generalized workflow for providing enhanced productrecommendations in accordance with the methods and systems outlinedabove. It is appreciated that the herein described workflow may beutilized in connection with any of the above-referenced embodiments.

As illustrated in FIG. 4, at item 1 of the exemplary workflow 400, aprofile question interface is provided to customers. The profilequestion interface comprises a graphic user interface (GUI) whichdisplays an interactive questionnaire to the customer. In one variant,various ones of the questions are preloaded with answers derived fromcustomer data, such as data collected and stored at the user dataservers 102. Verified answers to the customer questions and/or answersentered by the customer manually are stored at the profile database ofFIG. 4.

As demonstrated by item 2 of the workflow 400, a plurality of datasources provide data to a personalization/recommendation engine. In theillustrated example, an ecommerce transaction database, a customerrelationship management (CRM) database, a weather database, a feedbackdatabase, and a stylist database are provided. However, it isappreciated that any number and type of databases may additionally beincluded; the foregoing are merely exemplary of the general concepts ofthe present disclosure. Data from each of the foregoing databases aswell as the profile database generated at item 1 above is provided tothe personalization engine which, at item 3, weighs each of the dataprovided thereby and generates a per-item score.

Specifically, the personalization/recommendation engine takes intoaccount profile data from the profile database; sales or item popularitydata from the ecommerce transaction database; demographic data from thecustomer relationship management (CRM) database; temperature andprecipitation data from the weather database; user-selection feedbackdata from the feedback database; and stylist-selection feedback datafrom the stylist database. Additional data may be provided from adatabase for e.g., indicating sports seasonality, time since lastpurchase of each item type by a user, etc.

The item scores are stored at a score database of a personalizationservice at item 4 of the workflow 400 of FIG. 4. A stylist interface isprovided at item 5 of the workflow 400. According to item 5, the scoreinformation and catalog metadata are utilized to create a stylistinterface which provides relevant information about the personalizationengine-recommended products. In one embodiment, the interface is of thetype discussed above with respect to FIGS. 2A-2B. Scoring information isprovided to the curator to assist in selection of one or more productsfor delivery to the subscriber; metadata is used to display images ofthe products and to provide sizing recommendations (i.e., runs small,loose fit, etc.).

Information regarding the stylist selections is provided to a stylistfeedback database, which as noted above is taken into account whenfuture recommendations are made by the personalization engine.

Next, per item 6, the items which the stylist has selected are deliveredto the subscriber. The subscriber keeps and pays for items from theshipment which he/she elects to keep, and returns the items he/she isnot interested in keeping/purchasing. Information relating to the itemswhich the subscriber keeps and/or returns is provided at item 7 to afeedback database. As noted above, the customer feedback is taken intoaccount when future recommendations are made by the personalizationengine as well.

Alternate Recommendations and Learning

The following discussion relates to one particular implementation of thebroader concepts provided herein. This discussion is not limiting andrepresents one embodiment which may be utilized (or not utilized) inconnection with the methods and systems discussed elsewhere herein.

As noted elsewhere herein, in one embodiment the aforementioned healthparameter data and other data obtained from one or morehealth-monitoring applications may be utilized to generate one or moreconsumer profiles relating to each consumer. A subscriber or customerprofile is created for each customer and stored at the user and productprofile database 116 and/or the customer database 158. In one variant,the user profiles each comprises a table, matrix, or vector. Data storedin the profile is verified by the user via an on-boarding process asdiscussed above. In another alternative, or in addition, data for theuser profile is provided directly by the user via one or moreon-boarding questions.

It is appreciated that certain ones of the aspects relating to a usercomprise relatively fixed or static aspects, and certain other ones ofthe aspects relating to a user comprise relatively dynamic or changingaspects. In order to track and/or represent these disparate data types,in one variant, each user is associated to at least two user profilesone for each of the static and dynamic data.

The static aspects may include e.g., gender and/or gender identity whichmay be represented in the consumer profile as M or F. Other fixedaspects include e.g., pants size, shirt size, shoe size, etc. Thesefixed value aspects may be represented in the profile as their actualvalue (e.g., S, M, L, XL, size 11.5, etc.). The fixed value aspects are,in one embodiment, utilized in a comparison to filter in/out availableproducts based on whether each product is applicable/inapplicable to theparticular consumer. That is to say, when the static user profile iscompared to product records, products will be filtered in/out for agiven user based on e.g., whether they are intended for men or women,the available sizes, etc.

The static aspect profiles for each user which are generated via e.g.,cooperation among the recommendation engine 104, curator apparatus 106,user data server 102, user and product profile database 116, customerdatabase 158, and/or or other network 100 entity. In one variant, thestatic user profile information (e.g., shirt size, pant size, shoe size,gender, etc.) is derived from information obtained by the profilegenerating apparatus from data obtained about the user from the userdata server 102, user device 110, and/or customer database 158. Forexample, as noted above, the user may enter information into a profilefor one or more health-monitoring applications running at the userdevice 110. That information is stored at a user data server 102 and maythen be obtained by a profile generating apparatus in order toappropriately populate the static profile for that user. In one specificexample, the user may enter a gender when establishing an account with ahealth-monitoring application which is then used to populate the genderfield in that user's static profile. In another specific example, theuser may enter an age, height and weight when establishing an accountwithin a health-monitoring application. The profile generatingapparatus, may then use this information to derive the user's size. Inanother embodiment, the foregoing static user profile information isobtained from direct user input in a user onboarding process, asdiscussed elsewhere herein.

It is noted that, although not frequently, static user profiles may beupdated to reflect changes in the user's size, etc. In one embodiment,these changes are performed manually by a human operator for example atthe recommendation engine 104 or at the curator apparatus 106, either inresponse to a consumer request to do so, or based on informationreceived relating to returned products (i.e., products are returned apredetermined number of times with a report of being too large, toosmall, etc.). In another embodiment, the consumer him/herself may beprovided an interface 162 within which the user's various sizes andother static profile aspects may be changed (such as via the b2c backend164 and subscription management service of the recommendation servicesbackend 152). In another embodiment, a process running at therecommendation engine 104 and/or the curator apparatus 106 may beconfigured to identify based on the aforementioned feedback informationthat a user's size is incorrectly listed. For example, the process mayrecognize that the consumer/user consistently returns shirts which aresize large as being too big. After a certain number of such returns, theprocess will cause the user's shirt size to be decreased to mediumautomatically (without further curator, customer, or operatorintervention).

In one embodiment, a static aspect filtering is performed prior to acomparison of product records to the dynamic user profile aspects. Inthis manner, e.g., products which are not available in the user's sizeand/or which are intended for a different gender than that which theuser is or identifies with are not provided into the second comparisonstep (which will judge the level of interest the user may have in theproduct based on activities/sports, interests, recent purchases, etc.).

The static aspects may be held in a profile having any format whichfacilitates filtering as discussed above. In one embodiment, the staticaspects are held in an n×2 matrix, where n is a number of static aspectsrelating to a single consumer. Each matrix is also provided with aunique user identifier. An exemplary static user profile matrix,M_(user static) may comprise a first column describing each aspect:shirt size, pant size, shoe size, gender. It is appreciated that otherstatic aspects may also be represented in the static user profilematrix, the foregoing being merely exemplary of the concepts of thepresent disclosure. The exemplary user may for example be represented assize L or large in both shirts and pants and having a shoe size of 8. Inaddition, the exemplary user may be female (gender listed as F) forpurposes of this example.

The dynamic or changing aspects represented in the user profilescomprise interest-level dependent indicator values; i.e., values whichare dynamically adjusted in accordance with a user's interest level in agiven aspect. The dynamic user profile aspects may include any number ofsports or activity related interests as well as fashion preferencerelated aspects. Some examples include e.g., running, basketball, yoga,leisure, red, black, green, patterns, shorts, tights, etc. In oneembodiment, each aspect is represented by a weighted value; the weightedvalue increases or decreases over time in accordance with the user'sinterest or the applicability of the value to the particular user. Forexample, a user's profile may illustrate a high interest in running bydesignating the running aspect of his user profile as a value of 100,and a low interest in basketball by designating the basketball aspect ofhis user profile as a value of 0.

The dynamic aspects may, in one embodiment, also be held in as m×2matrixes, where m is a number of dynamic aspects relating to a singleconsumer. An exemplary dynamic user profile matrix, M_(user dynamic) maycomprise a first column describing each aspect: running, yoga, leisure,and basketball. It is appreciated that other dynamic aspects may also berepresented in the dynamic user profile matrix, the foregoing beingmerely exemplary of the concepts of the present disclosure. Continuingthe example, the particular user may have an interest level of 100 inrunning, 35 in yoga, 115 in leisure (such as so called “athleisure”wear), and no interest (0) in basketball.

The user identifier and/or other user-specific information within orassociated to the consumer profiles may be anonymized using acryptographic hash function. Alternatively, other means of ensuringconsumer/user data privacy may be implemented. Additionally, it is notedthat in another alternative, the static and/or dynamic user profilematrixes may be represented as n×1 or m×1 vectors listing only valuesfor the represented aspects. In this embodiment, a reference vector maybe utilized to orient an operator (such as the human curator discussedherein) with regard to the aspects represented.

In one variant, the dynamic user profiles are created based oninformation obtained from the user data server(s) 102. It is appreciatedthat according to this variant, when a user establishes an account withthe recommendation engine 104, the recommendation engine 104 pulls datafrom the user data server 102 within a designated time period (e.g., themost recent week, month, six months, etc.). From this data, therecommendation engine 104 is able to pre-populate a dynamic profile. Forexample, suppose a user has logged five one-hour running workouts in thelast seven days. In this case, the recommendation engine 104 will setthe user's interest in running as relatively high. In contrast, assumingthe same user logged only one half-hour yoga workout in the last month,the recommendation engine 104 will set the user's interest in yoga asrelatively low. Still further, assuming that the user has logged zerobasketball workouts in the last year, does not follow any basketballplayers on social media, and has otherwise expressed no interest inbasketball, the recommendation engine 104 will score basketball as azero in the dynamic user profile.

It is further noted that, in another variant, the original values forthe dynamic profile aspects may be manually entered by e.g., theuser/consumer, an operator at the recommendation engine 104, and/or anoperator at the curator apparatus 106. In addition, or alternatively,the dynamic aspect profile may begin according to a template based onone or more user-entered interests and/or other known usercharacteristics, e.g., gender. According to this model, these templatevalues are adjusted over time as additional information is obtained fromthe user data server(s) 102 (as discussed below).

Under any of the foregoing methods for developing the dynamic userprofile, it is appreciated that the dynamic profiles once created areupdated according to user activity. Profiles are updated periodically,in that data is pulled from the server 102 daily, weekly, monthly, etc.;alternatively, data may be provided as it is received/obtained/sensed.

In one embodiment, as user profile data is received at therecommendation engine 104, the current dynamic aspect values aremultiplied by a weighting factor to arrive at an updated weighted value.For example, if data is received indicating that the user has loggedthat he/she played basketball for a half hour in a health-monitoringapplication, the profile value of 0 for interest in basketball will beincreased. The amount of increase may be proportional to the correlationof the event (e.g., playing basketball for a half hour) to the aspect(e.g., basketball), as discussed in further detail below. The weightingvalues utilized here are, in one variant, manually entered by anoperator or curator. As noted above with regard to the score moduleweighting factors, in another embodiment, the recommendation engine 104may “learn” an appropriate weighting factor over time (as is alsodiscussed below). It is appreciated that the events and weightingfactors listed table below are intended for example purposes only andshould not be considered inclusive of all relevant events and/orpossible weighting factors.

In one specific example, when the subscriber logs a workout less than 30minutes, the weighting factor may be +5, whereas if the workout isgreater than or equal to 30 minutes, the weighting factor may be +15.Social media comments that contain positive reactions to particularactivities (e.g., sports, workouts, etc.) may be weighted by +2, whereasnegative social media comments are weighted by −2. In addition, thesystem may apply a weighting factor of −25 when a product is returned.In order to account for the length of time between purchases, in oneembodiment, a weighting factor of +1 may be applied for calculationsoccurring less than one month since the last purchase (of a particulartype of product or purchases generally); and a weighting factor of +32may be applied for calculations occurring more than one month since thelast purchase.

As noted, the collected/sensed data may be used to populate and/orupdate the dynamic user profile as appropriate. For example, a user'scontinued tracked running workouts in one of the aforementionedhealth-monitoring applications would be used to increase a weightedvalue indicative of an interest in running related products (e.g.,running shoes, running clothing, etc.). Moreover, the sensed orcollected data may be tracked over time such that a user's interest inan item increases from the date of a new purchase of the item. Forexample, running shoes have a limited lifespan; hence, the modeldiscussed here would take into account a date of last purchase ofrunning shoes and an average number of miles run per day to increase theuser's interest in a new pair of running shoes. The date of lastpurchase, in one embodiment, is determined from the date on which apurchase order was made by the user, such as a purchase order made inconnection with the herein described subscription service. In otherwords, the date on which running shoes were purchased would be known asthe date a curator selected and caused running shoes to be delivered tothe consumer. In another embodiment, the user may provide the date ofpurchase of the running shoes. Still further, such data may beascertained when the user makes a purchase from a connected application(e.g., a shopping application which configured to share information withone or more of the aforementioned health-monitoring applications) orusing a connected credit card/debit card. Similar logic applies to otherproducts such as e.g., tights/leggings, shirts, shorts, sweatshirts,accessories, etc.

In another example, social media posts may be used to populate and/orupdate the dynamic user profile. For example, a user's “likes” andfollows on a social media platform (such as one associated with one ofthe aforementioned health-monitoring applications) regarding aparticular basketball player would be used to increase a weighted valueindicative of an interest in basketball related products (e.g.,basketball shoes, basketball clothing, the identified player's jersey,etc.). Similarly, the content of the user's posts may be analyzed forapplicability to certain sports, celebrities, products, etc. Forexample, a user may write a post which states “I tried my first hot yogasession today, I loved it!” A processor running at e.g., therecommendation engine 104 analyzes the post to determine that mention ofan interest in yoga correlates to an increase in the weighted valueindicative of an interest in yoga; in addition, the overall positiveresponse (“ . . . I loved it!”) may be taken into consideration. Inanother example, the user may simply “check-in” to a yoga center and therecommendation engine 104 may utilize this information to increase theweighted value indicative of an interest in yoga.

In addition, other details regarding the user collected at accountregistration (i.e., when the user registers with one or morehealth-related applications or devices) may be utilized to populate theuser profile table, matrix, or vector. For example, when the userestablishes an account with a health-monitoring application the user maybe required to enter basic information such as gender, height and weight(from which sizes may be derived), and interest in particular athleticactivities, celebrities, etc. This information may be pulled from theuser data server 102 during creation of both the static and dynamic userprofiles.

As noted above, the user profiles are stored at e.g., the user andproduct profile database 116 and/or customer database 158. In addition,as also noted above, the product profile database 116 and/or the productdatabase 156 is configured to store a plurality of product profiles. Inone variant, each product profile comprises a table or p×2 matrix, wherep is a number of aspects relating to an available product. In onespecific embodiment, the number, p, of aspects of each product profilecorresponds to the number m of aspects of the dynamic user profile. Asnoted above, the product records are first filtered according to thestatic user profile. Accordingly, a means of identifying sizes andgender targeting of each product are given. In one embodiment, this issatisfied by creating a product record identifier or tag (such as e.g.,size S, M, L, etc.) on each product record. The recommendation engine104 may easily filter the product records prior to comparison accordingto the dynamic aspects.

Alternatively, the number, p, of aspects of the product profilescorresponds to the combined number, n+m, of aspects of each of thestatic and dynamic user profiles. According to this embodiment, therecommendation engine 104 knows that the first n number of aspects inthe profile comprise fixed value aspects and are to be compared to theuser's fixed profile and that the remaining aspects (p−n) correspond tothe dynamic aspects and are to be compared to the user's dynamicprofile. Similar logic applies in the instance the user profile includescombined static and dynamic aspects. In this manner a simple comparisonbetween the product profiles and an individual consumer profile isfacilitated. In one variant, this may be accomplished using a dotproduct between the two vectors (a user profile vector and an individualone of the product record vectors). A scalar quantity may be found (viathe aforementioned dot product calculation) for each product profile ascompared to a particular user's dynamic profile. Then, only thoseproducts which meet or exceed a predetermined threshold are recommended.

Static user profile updates, as discussed above, may be made manually orvia machine learning. Dynamic user profile updates utilize a weightingtable (such as that discussed above). Suppose for example, that a userhaving the dynamic profile from the example above (M_(user dynamic))logs a one hour run with his/her health-monitoring application. Toaccount for this event, an event record is created as a matrix or avector. Next, the event record is multiplied by the weighting factor.According to the example above, the activity (a workout of more than 30minutes) is weighted by +15. Hence, the event record is weightedmultiplied by positive 15. In order to update the user's profile, thenewly created weighted event record or vector is added to the usersdynamic profile vector thereby increasing the score for running from 100to 115. The updated user dynamic profiles may then be used to determinerelevant products as discussed below.

As noted elsewhere herein, the recommendation engine 104 generatesrecommendations (i.e., selects one or more of a plurality of productstargeted to a specific consumer) by comparing each of the productrecords to the consumer's profile via a comparison algorithm. In thevariant discussed herein, this occurs via mathematically determining ascalar quantity (via dot product) of two vectors (the product recordvector and the updated user profile vector) and comparing the scalarquantity to a threshold. Those products producing a scalar quantity ator above the threshold are added to a list of recommended products forthat consumer.

For example, suppose the system is to determine the relevance of ProductA having a product record of {running 100, yoga 5, leisure 25,basketball 50} to the dynamically updated user profile {running 115,yoga 35, leisure 25, basketball 0}. In order to do so, the scalarquantity associated for the comparison of the profile to Product A isdetermined. In this case, the scalar quantity would be:

$\begin{matrix}{{\begin{bmatrix}100 \\5 \\25 \\50\end{bmatrix} \cdot \begin{bmatrix}115 \\35 \\105 \\0\end{bmatrix}} = {\left( {100 \times 115} \right) + \left( {5 \times 35} \right) + \left( {25 \times 105} \right) + \left( {50 \times 0} \right)}} \\{= {{11,500} + 175 + {2,625} + 0}} \\{= {14,300}}\end{matrix}$

As noted above, the determined scalar quantity is then compared to athreshold to determine whether Product A should be recommended to theuser/consumer. For example, the threshold may be 10,000; in which caseProduct A will be recommended to the user (because it has a scalarquantity that exceeds the threshold). The specific thresholds, however,may be pre-loaded at the recommendation engine 104 or manually enteredby an operator of the recommendation engine 104 and/or curator.

As discussed above, the recommendation engine 104 is further configuredto utilize at least two feedback steps to ensure that itsrecommendations are in line with the user's preferences. The firstfeedback mechanism comprises utilization of feedback received from ahuman curator at a selection or curator apparatus 106. When the humancurator or operator makes a selection of one or more of the items in thelist, information about the selection is provided back to therecommendation engine 104. Additionally, information regarding itemsfrom the list which are not selected is gathered. In one variant, thecurator may additionally provide detailed descriptive informationrelating to his/her reasoning for selected/not selecting certainproducts. The second feedback mechanism comprises utilization offeedback received from the customer/subscriber. The returned items arereceived at the product warehouse 112, which reports back to the curatorapparatus 106 and/or the recommendation engine 104 information relatingto the returned products and/or the items which were not returned. Inaddition, the customer may additionally provide detailed descriptiveinformation relating to his/her reasoning for selected/not selectingcertain products. The feedback information is used by the recommendationengine 104 to adjust at least one of the user profile, one or moreproduct records, the comparison algorithm, and/or the threshold value asdiscussed herein. The curator apparatus 106 may additionally utilize theconsumer feedback information to assist the curator him/herself inmaking future selections (i.e., human learning).

In a first embodiment, when feedback information is received that a userdid not wish to purchase Product A, the user's (updated) dynamic profileis updated once again. In this instance, a weighted product record iscreated by multiplying the Product A vector by a weighting factorcorresponding to the return (e.g., −25 from Table 1 above), then addingthe weighted product record to the most recent dynamic profile for theuser to create an updated user dynamic profile.

In another embodiment, the product record itself is adjusted. Accordingto this embodiment, the weighted product record is created as indicatedabove, then such product record is used going forward in futurecomparisons to the user's profile. In addition, other product recordsmeeting at threshold level of similarity to that of Product A areidentified and likewise updated/weighted. Various mechanisms may be usedto determine similar products including, e.g., utilizing metadata orother tags to the product records to identify a product or sport line,using a dot product comparison of other product records to that ofProduct A, etc.

In yet another embodiment, the comparison algorithm itself may beupdated in response to the first and/or second feedback. In oneembodiment, this may include e.g., adding, subtracting, multiplying,dividing, or other performing any other mathematical operation to thescalar product and/or the vectors prior to applying the dot productformula.

In a further embodiment, the similarity threshold may be adjusted inresponse to the first and/or second feedback. That is, rather thanrecommending products having a scalar quantity greater than or equal to10,000, the recommendation engine 104 may adjust the threshold to anumber just above the scalar quantity that was determined for thereturned product, Product A, or in this case the threshold may become14,301 which is greater than the scalar quantity returned 14,300 whenProduct A was originally recommended. In the instance a plurality ofproducts are returned, the recommendation engine 104 may use the lowestscalar quantity score of the returned products (or the highest) toadjust the threshold in the manner outlined above.

Alternate Methodology

FIG. 5 below describes a method 500 which utilizes the alternateembodiment discussed above in order to enable enhanced recommendationsin accordance with the present disclosure.

As shown, per step 502, a plurality of product vectors are created. Inone embodiment, these are created at the recommendation engine 104 orany entity in communication therewith via the network 108. A productvector is created for each product available for purchase by consumersand includes information relating to its size and gender for which it isintended (where appropriate). In addition, each product vector comprisesone or more aspects which describe the character of the item. Forexample, a pair of running shoes would have a high score in runningcharacter, a medium score in leisure character, and a low score in yogacharacter.

Next, at step 504, user profile data is obtained. In one variant, theuser profile data is obtained from one or more user data servers 102.Each of the one or more user data servers 102 is configured to receiveactivity and profile data from a plurality of health-monitoringapplications in communication with or running on a user device 110. Therecommendation engine 104 or other apparatus in communication therewithconverts the user data to one or more user profile vector at 506.Specifically, for a given user, two user profiles may be created, onefor his/her static profile and the other for his/her dynamic profile(discussed above). In one specific embodiment, the static user profilescomprise n×1 vectors, the dynamic user profiles comprise m×1 vectors,and the product profiles comprise p×1 vectors. Still further, thenumber, p, of aspects of the product profiles corresponds to thecombined number, n+m, of aspects of each of the static and dynamic userprofiles in one variant.

Per step 508, the user profile vectors are updated as new activityand/or profile data is collected. For example, a user's weight may beobtained from a smart scale application and updated to the user dataserver 102. The recommendation engine 104 uses the updated informationto determine whether the user's shirt and/or pant size should beupdated. In another example, the user may log a first ever one hour yogasession, which is reported via a health-monitoring application to thedata server 102. In turn the recommendation engine 104 uses thisinformation (pulled from the data server 102) to update the user'sprofile to increase an interest in yoga. Detailed discussion of oneembodiment of how this may be accomplished is provided above.

Next, the most recently updated user profile(s) are compared to theproduct profiles to identify one or more products having a thresholdlevel of similarity to the user's profile(s) at step 510. In oneembodiment, as discussed above, products are filtered in/out based onthe static user profile aspects. In this manner, the remaining productscomprise only those which are applicable to the user/consumer based one.g., size and gender. A dot product is then calculated between aproduct vector of each of the remaining available products and theuser's dynamic profile. The scalar quantity (result of the dot productcalculation) is then compared to a threshold value. Product profileswhich produced a scalar quantity at or above the threshold are then, perstep 512 provided as a list of recommended products.

A human operator or curator at the curator apparatus 106 is providedwith the list of identified products and selects from the listindividual ones of the products (step 514) to be provided to auser/consumer. Feedback information is generated from the curator'sselections and, at step 516, the comparison, the threshold, the userprofile(s), and/or one or more product profiles are adjusted (asdiscussed elsewhere herein). In response to the curator selection, adelivery order is created for the product warehouse 112 which is used tocause delivery of the products to the user/consumer (step 518).

The user receives the products selected by the curator from among theitems identified by the recommendation engine 104. The user may selectcertain ones of the products to purchase and keep, and the remainingones of the delivered products are returned by the user to the warehouse112 (at step 520). Information about the user's selections is generatedat e.g., the warehouse 112 and provided to the recommendation engine 104and/or curator apparatus 106 which use the information at step 522 toadjust recommendations such as by adjusting a comparison, the threshold,the user profile(s) and/or one or more product profiles.

Additional Implementations

The foregoing methods and apparatus may be utilized as discussed aboveto provide product recommendations for consumers. As noted above,recommendations may be based on one or more of: weather, location,user-entered information (such as style and/or sport preferences), itempopularity, and/or sports season. In one variant, the item's popularitymay additionally relate to the item's momentum i.e., popularity spikes.For example, an item may a relatively high popularity at initialoffering, then tapers off over time; popularity may additionally spikedue to pop culture reasons (e.g., celebrity endorsement, team shirtsafter team wins big, etc.). It is further noted that in anotherembodiment, information from one or more connected applications (e.g.,health-monitoring applications) may be utilized to preload thepreviously referenced user-entered information.

In one embodiment, one or more patterns are utilized to identifyproducts which would be of interest to the subscriber. In oneimplementation, such patterns may be derived based on demographics. Forexample, it may be determined that women between ages 25-35 are morelikely to keep or purchase items having a particular style (e.g., short,strappy, colorful, etc.). Patterns may also be derived based ongeographic area or location (such as that entered during on-boarding, amailing address, and/or the customer's current location determined viaGPS, IP address, etc.). According to this implementation, it may bedetermined for example, that customers in a given geographic area mightutilize items which other geographic areas do not use as frequently;hence these are recommended. In another implementation, patterns may bederived from the subscriber's individual style. For instance it maybecome apparent that the subscriber e.g., doesn't like black, prefersbold patterns, doesn't like form fitting tops, etc. This information isused to inform future product selections.

In another implementation, patterns may be derived regarding anindividual's price points. That is to say, it may be determined based onthe subscriber's election to keep or return certain products that e.g.,the subscriber's price tolerance for a top is under $30 and for bottomsis $50; products are selected for recommendation accordingly. Stillfurther, a pattern may be derived based on particular item combinations.In other words, it may be determined that the success rate (i.e.,purchase rate) for Product A increases when it is sent to consumers atthe same time as Product B; stated differently, these two items pairwell or go together well. Such pattern determinations are then utilizedin future shipments to other consumers.

Consumers may also be grouped by primary sport and/or by primary andsecondary sport combinations. For example, a particular pair of shoesmay be most often selected by consumer's who primarily run.Alternatively, or in addition, a particular pair of tights may be oftenpurchased by runners who additionally take part in yoga workouts.

In yet another implementation, patterns may be derived based on dataobtained from other connected applications. For instance, certainhealth-monitoring applications may record and store a user's age, whichmay then be used to determine a pattern among similarly agedindividuals. In another instance, workouts logged in another connectedapplication may be scrutinized for patterns. For example, the hereindisclosed system may categorize runners into sprinters, short distancerunners, long distance runners, marathon trainers, etc. These categoriesare then used to identify purchase patterns; e.g., short distancerunners prefer shorts whereas long distance runners prefer tights, andso forth.

Additionally, patterns may be derived which take into account more thanone of the foregoing pattern bases. For example, it may be determinedthat women in the ZIP code 78701 are highly likely to purchase aspecific product or line or products or type of product. Hence, forwomen in the area the appropriate product(s) are selected.

Metrics may also be derived based specifically on a particular curator'ssuccess rates (i.e., the purchase rates of the consumers for whom thestylist curator products). For instance, it may be determined that aparticular curator has a high frequency of selecting items which arepurchased by the subscriber and which were not highly ranked by therecommendation engine 104. From this information, the recommendationengine 104 may learn which characteristics of a given product arecontributing to the curator's selection and subscriber's decision tokeep the item, then utilize the information to inform futurerecommendations. In another example, it may be determined that thecurator's selection of items which are not highly ranked by therecommendation engine 104 often results in products not being purchasedby the subscriber. In this instance, the recommendation engine 104 maysend an alert to the curator of the developing pattern and/or insteadrequire that the curator select at least one highly ranked item fordelivery to the subscriber.

Another embodiment relies on social media interactions of the subscriberand/or between the curator and the subscriber and/or between subscribersto derive information useful in making recommendations. In one specificimplementation, the system enables the curator to “friend” thesubscriber on one or more social media sites; for example, a socialmedia portion of any of the aforementioned health-monitoringapplications (including e.g., a connected health-monitoringapplication).

Once the connection is established between the curator and subscriber,the curator will be able to comment on the subscriber's social mediaactivity. For example, the curator can congratulate, motivate,encourage, etc., the subscriber's logged fitness activity and/oruploaded pictures (such as where the subscriber is wearing or using oneor more of the items selected by the curator). In addition, thesubscriber and curator may communicate directly regarding preferences,previously delivered items, upcoming activities, etc., such as via ahosted email or chat session. In another implementation, the curator isable to review the subscriber's health/fitness activity as entered bythe subscriber into the social media site. The stylist can then derivepatterns or categorize the customer's activity based on the observedheath/fitness activity, which are then used to select items to provideto the customer. In a further implementation, the curator may use the“friendship” to the subscriber to recognize when goals are met andcongratulate the subscriber. The curator may additionally determinebased on the subscriber's meeting a weight loss goal that the subscribershould be provided new (increased or decreased) sizes.

Another implementation enabled via the social media connection betweenthe curator and subscriber relates to the ability of the curator toreview the user's activity and make additional recommendations orobserve patterns which aren't as readily observable to therecommendation engine 104. In one specific implementation, the curatormay be able to provide items which are tailored to a typical time of dayin which the subscriber generally works out. In this manner, althoughthe subscriber may live in a typically warm climate, he/she may beprovided warmer clothing because the stylist has observed that thesubscriber primarily has workout sessions outdoors and in the earlymorning (which is much cooler than the average temperature in thatgeographic area).

In a further implementation, social media data regarding a particularsubscriber is used by the recommendation engine 104 and/or curator torecommend products which are specifically geared to that subscriber'sgoals and/or challenges. For example, assuming it is known that aparticular subscriber has signed up for a challenge to run 100 milesover a 3 month period, products may be recommended based on that goalincluding e.g., new running shoes.

In a further implementation, the curator is additionally connected toother social media feeds of the subscriber. Such social media feedsinclude e.g., Pinterest®, Facebook®, Twitter®, Instagram®, etc. Fromthese non-health monitoring applications, the curator may glean asubscriber's unique style. In another example, the stylist may reviewthe subscriber's likes and comments to derive patterns regardingupcoming events (e.g., going to mammoth) and learn about new sportsendeavors (e.g., tried out yoga). In yet another example, the curatormay determine from the lack of social media activity and/or actualstatements, that the user might be injured. In any of the aboveinstances, the stylist is able to recommend particular products based onthe social media gathered information (e.g., new styles, snow gear, yogagear, recovery products, etc.). Additionally, the recommendation engine104 may be configured to review the subscriber's social media activityand derive similar patterns.

In another implementation, the stylist and/or recommendation engine 104may review a subscriber's friend list to recommend products. Forinstance, common workout trends may be identified among a subscriber'sfriends (such as via logged workouts and/or check-ins) and in responsethe engine 104 and/or curator may recommend products relating to theworkout trend. In another instance, the recommendation engine 104 maydetermine from among the subscriber's friends, those which are alsosubscribers to the recommendation system, then use purchase/returntrends of a subscriber's friends to predict the subscriber's behavior.For example, all of a subscriber's friends who received Product A from acurator returned the item; accordingly the system may determine not tosend Product A to the subscriber (as he/she will likely also return it).

In yet another implementation, diversity in product selections may bebased off of a first subscriber's friends. In other words, rather thanproviding similar items to friends, items are selected to ensure thatfriends receive different items. For example, if User A receives therecommended item of a specific jacket, it will be appreciated thatfriends of User A would feel that their recommendations are lesspersonally tailored if they were to also receive the same jacket.Therefore, according to this embodiment, the system may decrease arecommendation score or rank of items which are provided to User A'sfriends. Additionally, or alternatively, items which are different thanthose which are provided to a user's friends are upweighted, so thatfriends each receive recommendations of different things

In another embodiment, patterns are developed across multiplesubscriber's social media feeds; in this instance these subscribers arenot necessarily “friends” as that term is used in a social media context(though they may be in certain embodiments). In one specificimplementation, a new exercise trend may be noticed across subscriberswhen various ones of the subscribers begin logging workouts,checking-in, and/or posting comments or pictures relating to the trend(e.g., Crossfit®, Orange Theory®, Barre®, etc.). In anotherimplementation, patterns of behavior of a first subscriber may bepredicted based on patterns expressed among other similarly situatedsubscribers. For example, it may be determined that subscribers whoparticipate in long distance runs (e.g., five miles or more per workout)supplement their workouts with yoga; whereas subscribers who participatein short distance runs (e.g., three miles of fewer per workout)supplement their workouts with basketball; accordingly, yoga-related orbasketball-related products may be provided to a subscriber based ontheir logged run workouts.

In a further implementation, the stylist may utilize the social mediaconnection to the subscribers he/she styles to create challenges eitherto an individual subscriber or to the entire group or sub-group thereof.In one variant, the challenge may be related to a particular physicalactivity or sport of interest to the individual/group. In anotheralternative, the challenge may be related to nutrition, sleep, meetinggoals, etc. The curator may also access and utilize information relatingto challenges that the subscriber's which he/she styles have entered.This information is utilized to recommend items that might be helpful tothe user in meeting the challenge (e.g., new shoes, running shorts,etc.). In addition, the stylist may offer a prize for the foregoingchallenges including, e.g., an item that is based on the serveddemographic, an item which the subscriber has previously “liked” but notpurchased, etc.

In another implementation, the curator may utilize social media topreview items for the subscribers he/she provides recommendations. Forexample, the curator may upload pictures, videos, text, etc. of thecurator using or wearing a particular item. The subscribers which followthe curator's social media feed may then comment on, like, request, etc.the items. The subscriber's reactions may then be utilized to assist inproviding further recommendations; the previewed items may additionallybe provided (i.e., such as part of their subscription box) to thesubscribers which “liked” the preview or otherwise provided positivefeedback. This implementation may be further extended to include anability of the subscribers themselves to upload pictures, videos, etc.of themselves wearing or using one or more of the recommended itemshe/she received. The subscriber's friends and/or a group of othersubscribers styled by the same stylist may then comment, like, request,etc. the item(s). The stylist then uses these comments to providefurther recommended products thereto. In addition, comments from thesubscriber's friends who are not current subscribers or are styled by adifferent stylist may be forwarded to e.g., a subscription onboardingpage or to the appropriate stylist.

Another social-based implementation includes querying the subscriber asto whether he/she would recommend the item to other subscribers. In oneembodiment, if the subscriber indicates that he/she would recommend theitem, an offer code, QR code, hyperlink, etc. may be provided for thesubscriber to pass on to friends so that they may purchase the item andmay include a discount to the subscriber if it is purchased and/or adiscount to the friends which purchase the item. In another embodiment,friends may select products/items for other friends. In suchembodiments, the first friend may select a particular item based oninformation that is known about the second friend as a result of theirfriendship, social media interactions, etc.; the first friend maypurchase the item for the second friend or merely recommend it;moreover, it may be included in a box of other recommended items orprovided separately therefrom. In addition, a note or gift message maybe provided with the item. In a further embodiment, a first subscribermay rank his/her friends based on a similarity of style and/orinterests. The subscriber's friends may also be subscribers to therecommendation service; or alternatively, may simply comprise socialmedia connections.

In a further implementation, data may be extrapolated from theaforementioned friend recommendations to be applied to strangers.Suppose for example that a first subscriber and second subscriber havegenerally similar profiles and historically similar item purchases butare not otherwise socially connected; accordingly, a recommendation ofan item by a friend to the first subscriber may trigger that same itemto be recommended by the curator and/or recommendation engine to thesecond subscriber.

As noted elsewhere herein, certain rules may be applied to the curator'srecommendations. For example, a total number of items may berequired/limited, a total number of each type of item may berequired/limited, temporal requirements may be implemented (e.g., nomore than 1 pair of shoes per 2 months, no same item type sent within 2months of last purchase of same item type, etc.). Additional rules maybe placed on the total price per item and/or per entire shipment.Moreover, certain item combinations may be required (e.g., at least onebottom and at least one top, etc.). Still further, the rules engine mayrequire that at least one key item or key trend (straps, color block,midriff, etc.) be provided in each box of recommended items. The keyitem, trend, and/or style items comprise items which pursuant to abusiness determination represent the brand, the season, etc. In afurther implementation, a three-dimensional rules engine for shipmentselections may be utilized wherein selection of a first particular itemcauses a number of additional items to be recommended as “going wellwith” the first selected item. In this manner, the recommended items arebased on selection of a previous item; all recommendations are seededbased on the curator's first sections.

In yet another specific implementation, products or items may be taggedbased on a particular subscriber's style and selection thereof. Forexample, if it is noted that a given number of subscribers who identifyas having a “classic” style select a particular item or specific type ofitem, that item or those item(s) are retroactively tagged as being“classic” in style. Thereafter, other subscribers who also identify ashaving a preference for “classic” style will have the items recommendedthereto as being within the “classic” style.

In another implementation, the foregoing systems and methods may beutilized to provide a recommendations of only discounted items (such asoutlet, warehouse, clearance, sale, etc. items) and/or the foregoingdiscounted items may be included in the aforementioned recommendationsubscription. For example, the rules engine may dictate a minimum ormaximum number of discounted items to be provided. In anotherembodiment, the discount may be applied to items which have beenpreviously provided/recommended to a predetermined number of othersubscribers, and which were returned without purchase (i.e., multiplereturn from previous recommendations).

Still further, the foregoing systems and methods may be utilized in arental recommendation subscription. In one example, the recommendedproducts may be provided to the subscriber for wear and use for apredetermined period of time and/or for as long as the subscriber wouldlike to hold on to them. In one variant, the customer may return the allor some of the products at any time or on expiration of the rentalperiod; and keep for purchase (including purchase at a discounted rate)those items which he/she would not like to return.

In another implementation, lifetime and/or lifestyle events are used asa basis for recommending products. For example, it may be determinedfrom social media data and/or data directly from the relevant eventadministrators/coordinators (such as upon authorization of theparticipant), that a subscriber has signed up for an event such as e.g.,the Tough Mudder®, the Boston Marathon®, etc. In another example, it maybe determined from social media data, onboarding data, etc. that asubscriber likes or is a fan of a particularfigure/athlete/celebrity/team; when that person/team becomes champion,wins, makes all-star team, etc. products may be recommended to thesubscriber which are related to the accomplishment. In another variant,products related to events which are geographically near a subscriberare recommended irrespective of the particular subscriber's interest inthe particular teams, players, etc. In a further variant, products maybe recommended which are associated to city affiliated teams, players,and/or celebrities of old addresses and/or places the subscriber mayhave lived in the past.

Continuing the above example, other lifetime and/or lifestyle eventswhich may be utilized to recommend products include e.g., birthdays,weddings, new baby, festivals and conventions, hospital visits; dataindicative of the foregoing may be obtained from e.g., a subscriber'scalendar, social media posts, private messages with the stylist,on-boarding data, etc. In addition, for these events, stylists mayprovide items of particular interest to the subscriber, recovery relatedproducts, etc.; and in some instances certain items may be provided asgifts e.g., free of charge. IN another variant, products may berecommended based on upcoming holidays for which people traditionallyprepare (such as spring break, summer break, etc.), holidays in which itis traditionally harder to maintain fitness goals (e.g., winterholidays) and/or new fitness goals are created (e.g., new year).

In another implementation, data may be collected relating to one or moresubscribers involvement in a team (e.g., a race team, walk team, cyclingteam, Team-in-Training®, Walk for the Cure®, Race for the Cure®, SpecialOlympics®, etc.) and a team specific shipment or delivery includingitems selected by e.g., the stylist, a team member, an eventcoordinator, etc. is provided. In another variant, the team subscriber'ssubscription may be limited to the training period only such that duringthe training period they receive recommended products and at the end ofthe training period, prior to an event, a special completion package ofrecommended products is provided. In a further embodiment, teamsubscribers may be provided special offers or discounts to transition toa general membership/subscription upon completion of the teamsubscription.

Additional implementations may include utilizing feedback and learningmechanisms to organize colors into groups or palettes. In one specificvariant, this may be accomplished via placement of all of the colorsused on clothing and (optionally) accessories into color umbrellas viautilization of metadata tags. Colors may be related using theaforementioned metadata tags. Hence it may be determined for example,that items with color A often also include color B. Hence, the systemmay recommend items with color A when an item having color B is selectedby the curator. In another variant, color groupings may be learned basedon stylist and/or consumer selections. For example, it may be derivedthat the curator and/or customer often selects items either having bothcolors A and B or individual items having color A and color B in thesame purchase order. From this information, the recommendation enginemay recommend items that have color A when items that have color B areselected by the curator and/or customer. In addition, the determinedpatterns may be used by the curator to ensure future recommendations forall subscribers include items which are “coordinated” in color, i.e.,include colors which are often selected together.

In another implementation, a user's profile may be examined by e.g., therecommendation engine and/or the curator to identify patterns which donot tend to align, and then make product recommendations based thereon.For example, suppose a particular subscriber has never logged a swimmingworkout, then logs a completed triathlon. From this, the system maydetermine to recommend swimming related products to the subscriber. In afurther implementation, a user's profile may reveal to the curatorand/or recommendation engine milestones or achievements of thesubscriber. For example, the subscriber has logged 1,000 miles biking,walking, running, swimming, or entered 1,000 hours of gym-basedexercises, etc.

In yet another implementation, other products may be recommended and/orprovided to subscribers via the herein described recommendation system(i.e., recommendation engine and human curator). For example,educational/instructional content may be provided in the form of:physical printed materials, digital content available at the userinterface, and/or digital content which is accessed via a hyper link, QRcode, etc. In another example, free passes or discount offers forparticular fitness classes (whether digital or physical) may be sent orrecommended. In yet another example, free trial periods or discountoffers for subscription services may be provided (such as upgraded orpremium membership to fitness tracking applications). In anotherexample, a physical or digital lookbook may be provided based on therecommended items. The lookbook may include mix and match options andstyling tips for the clothing and/or accessory products.

Finally, the recommendation system may further incorporate coachingfeatures, such as via the curator, computer program, or other trainedprofessional. In one embodiment, coaching is provided as a recommendeditem at discounted rates and/or one or more free sessions may beincluded among recommended items. Such coaching may include emotionalsupport, digital coaching, and/or nutrition coaching.

Each of the foregoing implementations may be utilized in connection withany other one or ones of the other discussed implementations. Theforegoing examples are described to be modular in nature, therebyenabling a vast landscape of possible implementations to be derived.

Exemplary Apparatus

An exemplary recommendation engine 104 and curator apparatus 106 areillustrated and discussed below with respect to FIGS. 6 and 7,respectfully. It is appreciated, however, that the functionality ofthese two apparatus may be combined into a single apparatus such thatone or more processors running on the combined apparatus are configuredto perform the recommendation engine-related processing (as discussedbelow with respect to the recommendation engine 104) and the curatorapparatus-related processing (as discussed below with respect to thecurator apparatus 106). A user interface as discussed below for enablingthe curator to interact with the curator-related processing is alsoprovided in the combined apparatus.

Referring now to FIG. 6, an exemplary recommendation engine 104configuration for providing enhanced product recommendations inaccordance with one embodiment of the present disclosure is given. Therecommendation engine 104 may comprise a computerized apparatus whichautomatically recommends one or more products to individual users. Asshown, the recommendation engine 104 generally includes a storageapparatus 606 and one or more processors 602 and network interfaces 604.Other components of the recommendation engine 104 may additionally beprovided to ensure the functioning thereof (not shown).

The network interfaces 604 enable communication between therecommendation engine 104 and various network 108 entities includinge.g., the curator apparatus 106, the user device 110, the user andproduct profile database 116, the customer database 158, the productdatabase 156, and the user data server(s) 102. As noted above, in oneembodiment, the network 108 to which the network interfaces 604communicate comprises a wired and/or wireless, private and/or publicnetwork, including e.g., the Internet. Hence, the interfaces 604themselves may comprise any appropriate networking communicationinterfaces for communication over the network 108.

The recommendation engine 104 further includes one or more processors orprocessor cores 602 configured to run various computer applicationsthereon, which may be stored at e.g., the storage apparatus 606. For thepurpose of this application, including the claims, the terms “processor”and “processor cores” may be considered synonymous, unless the contextclearly requires otherwise. Additionally, the storage apparatus 606 mayinclude mass storage devices such as diskette, hard drive, compact discread only memory (CD-ROM) and so forth. Additional features of therecommendation engine 104 may include e.g., input/output devices (suchas display, keyboard, cursor control and so forth) and additionalcommunication interfaces (such as network interface cards, modems and soforth), not shown. Moreover, the elements may be coupled to each othervia system bus including one or more bridged busses (not shown).

The computer applications run by the processor 602 include one or moreof: a profile processing application 608 and an analysis andrecommendation application 610 which further comprises a feedbackadjustment mechanism 612. Additional software applications and processesmay be run at the processors 602 as well; the foregoing are merelyexemplary. Moreover, the functionality described as attributable to oneor more of the foregoing applications may be combined into fewerapplications and/or a single application.

The profile processing application 608 comprises a software processwhich enables the recommendation engine 104 to generate a plurality ofuser and/or product profiles. As discussed above, a product profile iscreated for every available product. The profiles are built based oninformation about the product from other product data sources. Alsodiscussed above, each user may be associated to two distinct userprofiles, a static profile and a dynamic profile. As noted above, theuser profiles may be created using information pulled from the user dataserver(s) 102. It is appreciated that in another embodiment, therecommendation engine 104 does not generate the herein describedprofiles, rather another entity (not shown) in communication with therecommendation engine 104 is charged with generating these.

Once created, the profiles are stored at a user and product profiledatabase 116, and/or the customer database 158. The profile processingapplication 608 is further configured to update the user and productprofiles such as, in response to newly collected activity and profiledata. The profile processing application 608 may therefore causeperiodic requests for newly sensed or monitored activity and profiledata to be sent to e.g., the user data server(s) 102, and/or the userdevices 110. The profile processing application 608 then accesses apreviously created profile (created at the recommendation engine 104 orelsewhere), and utilizes a mechanism for weighting user activity eventsto increase/decrease a user's interest in one or more tracked aspects(such as e.g., running, leisure, yoga, basketball, etc.), such as themechanism provided elsewhere herein.

The analysis and recommendation application 610 comprises a softwareprocess which enables the recommendation engine 104 to perform acomparison of a specific consumer's user profile(s) to each of theavailable product profiles. In one exemplary embodiment, this isperformed via one or more of: (i) filtering in/out product profilesbased on the consumer's static aspects; (ii) calculation of a scalarquantity via a dot product of a user dynamic profile vector and each ofthe filtered product profile vectors; (iii) comparison of the calculatedscalar quantities to a threshold; and/or (iv) performing thecalculations associated with the previously described score modules.Those ones of the product records which have a scalar quantity or anoverall score at or above a given threshold are then listed asrecommended products.

The analysis and recommendation application 610 further comprises afeedback adjustment mechanism 612 which comprises a software processwhich enables the recommendation engine 104 to utilize feedback toadjust one or more of: the calculation of recommended items, a thresholdvalue for determining similarity of a product record to a user profile,the user profile(s), and/or the product records. The feedback mechanism612 may be responsive to a first round of feedback based on datarelating to a curator's selection from among a set of recommended itemsand a second round of feedback based on data relating to a user'sselection from among a set of delivered items. Still further, thefeedback mechanism 612 may weigh the curator's selections with more orless strength based on information obtained from the consumer himselfand/or other factors. For example, the curator may decide that the userwould not prefer patterned clothing; this feedback may be bolstered bythe user's subsequent remarks that he/she would not prefer patternedclothing (if such is ultimately provided).

Referring now to FIG. 7, an exemplary curator apparatus 106configuration for providing enhanced product recommendations inaccordance with one embodiment of the present disclosure is illustrated.The curator apparatus 106 may comprise a computerized apparatus withwhich a curator entity interacts to effect delivery of recommended andselected products to a consumer. As illustrated, the curator apparatus106 includes a storage apparatus 706, and one or more user interfaces708, network interfaces 704, and processors 702 configured to run one ormore software applications thereon (discussed below). The curatorapparatus 106 further includes various other features which ensurefunctioning thereof (not shown). For example, the curator apparatus 106may include e.g., input/output devices (such as display, touchpad,keyboard, cursor control and so forth) and additional communicationinterfaces (such as network interface cards, modems and so forth), notshown. Moreover, the elements may be coupled to each other via systembus including one or more bridged busses (not shown).

The storage apparatus 706 of the device 101 is utilized to storeinformation relating to a plurality of applications running on thecurator apparatus 106. The storage apparatus 706 may include massstorage devices such as diskette, hard drive, compact disc read onlymemory (CD-ROM) and so forth.

The user interfaces 708 comprise means by which a user can interact withvarious ones of the applications or programs on the curator apparatus106. In one embodiment, a graphic user interface (GUI) is displayed tothe user via a display apparatus, which may be located within thecurator apparatus 106 or separate therefrom.

The network interfaces 704 enable communication between the curatorapparatus 106 and various network 108 entities including e.g., therecommendation engine 104, the user device 110, the user and productprofile database 116, the customer database 158, the product database156, and the user data server(s) 102. As noted above, in one embodiment,the network 108 to which the network interfaces 504 communicatecomprises a wired and/or wireless, private and/or public network,including e.g., the Internet. Hence, the interfaces 504 themselves maycomprise any appropriate networking communication interfaces forcommunication over the network 108.

The curator apparatus 106 further includes one or more processors orprocessor cores 702 configured to run various computer applicationsthereon, which may be stored at e.g., the storage apparatus 706. Thestorage apparatus 706 may include mass storage devices such as diskette,hard drive, compact disc read only memory (CD-ROM) and so forth. Thecomputer applications run by the processor 702 include a productselection application 710 having a feedback adjustment mechanism 712.Additional software applications and processes may be run at theprocessors 702 as well; the foregoing are merely exemplary.

The product selection application 710 comprises a software process whichenables the curator apparatus 106 to provide a means by which anoperator/curator may select certain ones of the products recommended bythe recommendation engine 104 for delivery to the consumer.Specifically, the product selection application 710 causes generation ofthe GUI which is displayed to an operator of the curator apparatus 106(e.g., a curator). The curator selects from a selectable list ofrecommended products (recommended by the recommendation engine 104); thecurator's selections are made based on knowledge and intuition of thecurator him/herself. As noted elsewhere herein, selection of certainones of the recommended products made by the curator via the GUI causesgeneration of a delivery order. The delivery order, once completed, isthen provided to the product warehouse 112 and the appropriate productsare caused to be shipped to the consumer. The GUI may further enable thecurator to enter one or more reasons for his/her decision to select ornot select particular items. The product selection application 710 isconfigured, in one embodiment, to provide information relating to theselected and not selected ones of the recommended products to therecommendation engine 104 as feedback to be used in sharpening therecommendations produced thereby.

The product selection application 710 further provides a means by whichan operator/curator may communicate with the consumer. Specifically,when a human curator is matched to a particular consumer, in oneembodiment, the curator may exchange messages with the consumer todetermine his/her likes, interests, style, etc. In another embodiment,the product selection application 710 provides an interface whereby thecurator can “follow” the consumer on various social media sites (such asthose associated with the aforementioned heath-monitoring applications).In this manner, the curator may be altered when a user posts to his/hersocial media account and the curator may access the user's photos,postings, etc. and communicate to the user regarding purchased items,sports or activities of interest, etc.

The product selection application 710 further comprises a feedbackadjustment mechanism 712 which comprises a software process whichenables the curator to utilize feedback to adjust his/her futureselections for that consumer. Specifically, the feedback mechanism 712,in one embodiment, displays a listing of which delivered items werepurchased by the consumer, which were returned, and/or informationprovided by the user relating to a reason for the purchase or return ofeach item. The curator/operator may further utilize the feedbackmechanism 712 to generate statistics and graphic representations of theuser's purchase/return trends. The human operator/curator uses theinformation to inform his/her subsequent selections to that consumer.

In another embodiment, as noted above, the profile processingapplication 608, analysis and recommendation application 610 (includingthe feedback adjustment mechanism 612), and the product selectionapplication 710 (including the feedback adjustment mechanism 712) arerun as one or more applications at a combined recommendation and curatordevice (not shown).

In other embodiments, individual ones of the foregoing applications(608, 610, 612, 710, and 712) may be a launched via a generic browser,such as Internet Explorer, available from Microsoft Corp., of Redmond,Wash., or Safari from Apple Computer of Cupertino, Calif., e.g., such asin cases where the recommendation engine 104 and/or curator apparatus106 comprises a desktop or tablet computer.

In another embodiment, a permanent copy of the programming instructionsfor individual ones of the aforementioned applications (608, 610, 612,710, and 712) may be placed into permanent storage devices (such ase.g., the storage apparatus 606 and/or storage apparatus 706) duringmanufacture thereof, or in the field, through e.g., a distributionmedium (not shown), such as a compact disc (CD), or throughcommunication interface 604/704 (from a distribution server (notshown)). That is, one or more distribution media having animplementation of the agent program may be employed to distribute theagent and program various computing devices.

The herein described applications (e.g., applications 608, 610, 612,710, and 712) improve the functioning of the recommendation engine 104and/or curator apparatus 106, respectively or in combination by enablingit/them to provide enhanced product recommendations. Furthermore,devices that are able to provide recommendations using a two part systemincluding both automatically generated recommendations and personalizedhuman selected recommendations can operate more efficiently to providehighly personal and targeted products to a user and to improve accuracyof the automatic portion of the recommendation generation process via atwo-part feedback mechanism.

In summary, a method of enabling product recommendations is disclosed.In one embodiment, the method comprises (i) receiving a plurality ofproduct records and a plurality of user profiles, each of the pluralityof product records and each of the plurality of user profiles comprisingone or more aspects; (ii) applying a comparison algorithm to the one ormore aspects of an individual one of the user profiles and the one ormore aspects of each of the plurality of product records to identify asubset thereof which exhibit a threshold level of similarity; (iii)providing the identified subset of the plurality of product records to aselection entity, the selection entity configured to select firstproducts from the identified subset for delivery to a consumer; (iv)utilizing information received from the selection entity whichidentifies second ones of the identified subset which were not selectedto adjust one or more first factors of the comparison algorithm; and (v)utilizing information received from a product warehouse which identifiesone or more of the first products which were delivered to the customerand subsequently returned to adjust one or more second factors of thecomparison algorithm.

In another embodiment, the method comprises: (i) generating a pluralityproduct records representative of a respective plurality of products forpurchase, each of the product records comprising a number of descriptiveaspects; (ii) obtaining consumer profile data relating to an individualconsumer; (iii) converting the consumer profile data into a consumerprofile record comprising a number of descriptive aspects; (iv)utilizing one or more of the descriptive aspects of the consumer profileto filter out a subset of the plurality of product records; (v)identifying, via a comparison of the consumer profile record againsteach of a subset of the plurality of product profile records which werenot filtered out, one or more products which meet a threshold level ofsimilarity to the consumer profile data; (vi) providing a list of theidentified one or more products to a human operator; (vii) enabling thehuman operator to select from the list individual ones of the identifiedone or more products; (viii) generating a delivery order of the selectedindividual ones of the identified one or more products for delivery tothe consumer; and (ix) adjusting at least one factor of the comparisonbased on the human operator selections.

In addition, an apparatus configured to provide product recommendationsis disclosed. In one embodiment, the apparatus comprises: at least onenetwork interface configured to receive a plurality of product recordsand a user profile; a storage apparatus; and a processor configured toexecute at least one computer program, the computer program comprising aplurality of instructions which are configured to, when executed by theprocessor, cause the apparatus to: (i) apply a comparison algorithm toone or more aspects of the user profile and one or more aspects of eachof the plurality of product records to identify a subset thereof whichexhibit a threshold level of similarity to the user profile; (ii)provide the subset of the plurality of product records as a selectablelist to a human operator via the interface, selection of individual onesof the selectable list by the human operator causing generation offeedback information regarding whether each of the subset was selectedor not selected by the human operator and generation of a delivery orderfor delivery of products associated with the selected individual ones ofthe selectable list to a user associated to the user profile; and (iii)utilize the feedback information to adjust one or more factors of thecomparison algorithm.

It will be appreciated that the various ones of the foregoing aspects ofthe present disclosure, or any parts or functions thereof, may beimplemented using hardware, software, firmware, tangible, andnon-transitory computer readable or computer usable storage media havinginstructions stored thereon, or a combination thereof, and may beimplemented in one or more computer systems.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodiments ofthe disclosed device and associated methods without departing from thespirit or scope of the disclosure. Thus, it is intended that the presentdisclosure covers the modifications and variations of the embodimentsdisclosed above provided that the modifications and variations comewithin the scope of any claims and their equivalents.

What is claimed is:
 1. A method of enabling product recommendations, said method comprising: receiving a plurality of product records and a plurality of user profiles, each of said plurality of product records and each of said plurality of user profiles comprising one or more aspects; applying a comparison algorithm to said one or more aspects of an individual one of said user profiles and said one or more aspects of each of said plurality of product records to identify a subset thereof which exhibit a threshold level of similarity; providing said identified subset of said plurality of product records to a selection entity, said selection entity configured to select first products from said identified subset for delivery to a consumer; utilizing information received from said selection entity which identifies second ones of said identified subset which were not selected to adjust one or more first factors of said comparison algorithm; and utilizing information received from a product warehouse which identifies one or more of said first products which were delivered to said customer and subsequently returned to adjust one or more second factors of said comparison algorithm.
 2. The method of claim 1, wherein said one or more first factors of said comparison algorithm comprise at least one of: said threshold, said comparison algorithm, said one or more aspects of said individual one of said user profiles, and/or said one or more aspects of said second ones of said identified subset.
 3. The method of claim 1, wherein said one or more second factors of said comparison algorithm comprise at least one of: said threshold, said comparison algorithm, said one or more aspects of said individual one of said user profiles, and/or said one or more aspects of said second ones of said identified subset.
 4. The method of claim 1, wherein said one or more aspects of said plurality of product records comprise at least one of: a product name, a product type, a product gender, a product size availability, a sports category, and an activity level.
 5. The method of claim 1, wherein said one or more aspects of said plurality of user profiles comprise at least one of: a user identifier, a user gender, a user size, a sports category, and an activity level.
 6. The method of claim 1, wherein each of said plurality of product records comprises a vector including said one or more aspects and each of said plurality of user profiles comprises a vector including a same number of said one or more aspects as said vectors of said plurality of product records.
 7. The method of claim 6, wherein said comparison algorithm comprises calculating a dot product of a particular one of said plurality of user profiles and each of said product records to produce a scalar quantity, and comparing said scalar quantity to said threshold value.
 8. The method of claim 1, wherein said first products are delivered to said consumer periodically as a subscription-based service and without further input from said consumer.
 9. A method of enabling product recommendations, said method comprising: generating a plurality product records representative of a respective plurality of products for purchase, each of said product records comprising a number of descriptive aspects; obtaining consumer profile data relating to an individual consumer; converting said consumer profile data into a consumer profile record comprising a number of descriptive aspects; utilizing one or more of said descriptive aspects of said consumer profile to filter out a subset of said plurality of product records; identifying, via a comparison of said consumer profile record against each of a subset of said plurality of product profile records which were not filtered out, one or more products which meet a threshold level of similarity to said consumer profile data; providing a list of said identified one or more products to a human operator; enabling said human operator to select from said list individual ones of said identified one or more products; generating a delivery order of said selected individual ones of said identified one or more products for delivery to said consumer; and adjusting at least one factor of said comparison based on said human operator selections.
 10. The method of claim 9, further comprising providing said delivery order to a product warehouse, said product warehouse causing said identified one or more products to be delivered to said consumer.
 11. The method of claim 10, further comprising: receiving, from said product warehouse, information relating to a return of certain ones of said products delivered thereto; and adjusting at least one second factor of said comparison based on said return of said certain ones of said products delivered to said consumer.
 12. The method of claim 11, wherein said information relating to a return further comprises descriptive information entered by said consumer relating to a reason for said return; wherein said act of adjusting said at least one second factor of said comparison is based on said descriptive information.
 13. The method of claim 9, wherein said consumer profile data is obtained from at least one social media-enabled application via network communication thereto.
 14. The method of claim 13, further comprising periodically updating said consumer profile data according to social media posts and/or changes made by said consumer.
 15. The method of claim 9, further comprising enabling said human operator to enter descriptive information related to individual ones of said identified one or more products which were not selected; wherein said act of adjusting said at least one factor of said comparison is based on said descriptive information.
 16. An apparatus configured to provide product recommendations, said apparatus comprising: at least one network interface configured to receive a plurality of product records and a user profile; a storage apparatus; and a processor configured to execute at least one computer program, said computer program comprising a plurality of instructions which are configured to, when executed by said processor, cause said apparatus to: apply a comparison algorithm to one or more aspects of said user profile and one or more aspects of each of said plurality of product records to identify a subset thereof which exhibit a threshold level of similarity to said user profile; provide said subset of said plurality of product records as a selectable list to a human operator via said interface, selection of individual ones of said selectable list by said human operator causing: generation of feedback information regarding whether each of said subset was selected or not selected by said human operator; and generation of a delivery order for delivery of products associated with said selected individual ones of said selectable list to a user associated to said user profile; and utilize said feedback information to adjust one or more factors of said comparison algorithm.
 17. The apparatus of claim 16, wherein said user profile is obtained from at least one social media-enabled application and said plurality of instructions are further configured to, when executed by said processor, cause said apparatus to convert said user profile into a same format as said plurality of product records.
 18. The apparatus of claim 16, wherein said adjustment of said one or more factors of said comparison algorithm comprises adjustment of at least one of: said threshold, said comparison algorithm, said one or more aspects of said user profile, and/or said one or more aspects of said subset of said plurality of product records.
 19. The apparatus of claim 16, wherein said delivery of said products associated with said selected individual ones of said selectable list occurs periodically as a subscription-based service and without further input from said user.
 20. The apparatus of claim 16, said plurality of instructions are further configured to, when executed by said processor, cause said apparatus to: provide said delivery order to a product warehouse, said product warehouse causing said products associated with said selected individual ones of said selectable list to be delivered to said user; receive, from said product warehouse, information relating to a return of certain ones of said products delivered to said user; and adjusting at least one second factor of said comparison based on said return. 